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Regression.</title> <meta name="description" content="Search results for: Regression."> <meta name="keywords" content="Regression."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Regression."> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 763</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Regression.</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">763</span> Relationship between Sums of Squares in Linear Regression and Semi-parametric Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dursun%20Ayd%C4%B1n">Dursun Ayd谋n</a>, <a href="https://publications.waset.org/search?q=Bilgin%20Senel"> Bilgin Senel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the sum of squares in linear regression is reduced to sum of squares in semi-parametric regression. We indicated that different sums of squares in the linear regression are similar to various deviance statements in semi-parametric regression. In addition to, coefficient of the determination derived in linear regression model is easily generalized to coefficient of the determination of the semi-parametric regression model. Then, it is made an application in order to support the theory of the linear regression and semi-parametric regression. In this way, study is supported with a simulated data example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Semi-parametric%20regression" title="Semi-parametric regression">Semi-parametric regression</a>, <a href="https://publications.waset.org/search?q=Penalized%20LeastSquares" title=" Penalized LeastSquares"> Penalized LeastSquares</a>, <a href="https://publications.waset.org/search?q=Residuals" title=" Residuals"> Residuals</a>, <a href="https://publications.waset.org/search?q=Deviance" title=" Deviance"> Deviance</a>, <a href="https://publications.waset.org/search?q=Smoothing%20Spline." title=" Smoothing Spline."> Smoothing Spline.</a> </p> <a href="https://publications.waset.org/11034/relationship-between-sums-of-squares-in-linear-regression-and-semi-parametric-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11034/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11034/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11034/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11034/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11034/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11034/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11034/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11034/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11034/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11034/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11034.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1854</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">762</span> A Comparison of the Sum of Squares in Linear and Partial Linear Regression Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dursun%20Ayd%C4%B1n">Dursun Ayd谋n</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, estimation of the linear regression model is made by ordinary least squares method and the partially linear regression model is estimated by penalized least squares method using smoothing spline. Then, it is investigated that differences and similarity in the sum of squares related for linear regression and partial linear regression models (semi-parametric regression models). It is denoted that the sum of squares in linear regression is reduced to sum of squares in partial linear regression models. Furthermore, we indicated that various sums of squares in the linear regression are similar to different deviance statements in partial linear regression. In addition to, coefficient of the determination derived in linear regression model is easily generalized to coefficient of the determination of the partial linear regression model. For this aim, it is made two different applications. A simulated and a real data set are considered to prove the claim mentioned here. In this way, this study is supported with a simulation and a real data example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Partial%20Linear%20Regression%20Model" title="Partial Linear Regression Model">Partial Linear Regression Model</a>, <a href="https://publications.waset.org/search?q=Linear%20RegressionModel" title=" Linear RegressionModel"> Linear RegressionModel</a>, <a href="https://publications.waset.org/search?q=Residuals" title=" Residuals"> Residuals</a>, <a href="https://publications.waset.org/search?q=Deviance" title=" Deviance"> Deviance</a>, <a href="https://publications.waset.org/search?q=Smoothing%20Spline." title=" Smoothing Spline."> Smoothing Spline.</a> </p> <a href="https://publications.waset.org/9806/a-comparison-of-the-sum-of-squares-in-linear-and-partial-linear-regression-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9806/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9806/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9806/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9806/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9806/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9806/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9806/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9806/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9806/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9806/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9806.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1872</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">761</span> A Comparison of the Nonparametric Regression Models using Smoothing Spline and Kernel Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dursun%20Aydin">Dursun Aydin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper study about using of nonparametric models for Gross National Product data in Turkey and Stanford heart transplant data. It is discussed two nonparametric techniques called smoothing spline and kernel regression. The main goal is to compare the techniques used for prediction of the nonparametric regression models. According to the results of numerical studies, it is concluded that smoothing spline regression estimators are better than those of the kernel regression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Kernel%20regression" title="Kernel regression">Kernel regression</a>, <a href="https://publications.waset.org/search?q=Nonparametric%20models" title=" Nonparametric models"> Nonparametric models</a>, <a href="https://publications.waset.org/search?q=Prediction" title="Prediction">Prediction</a>, <a href="https://publications.waset.org/search?q=Smoothing%20spline." title=" Smoothing spline."> Smoothing spline.</a> </p> <a href="https://publications.waset.org/4537/a-comparison-of-the-nonparametric-regression-models-using-smoothing-spline-and-kernel-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4537/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4537/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4537/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4537/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4537/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4537/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4537/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4537/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4537/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4537/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4537.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3101</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">760</span> Orthogonal Regression for Nonparametric Estimation of Errors-in-Variables Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Anastasiia%20Yu.%20Timofeeva">Anastasiia Yu. Timofeeva</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Grade%20point%20average" title="Grade point average">Grade point average</a>, <a href="https://publications.waset.org/search?q=orthogonal%20regression" title=" orthogonal regression"> orthogonal regression</a>, <a href="https://publications.waset.org/search?q=penalized%20regression%20spline" title=" penalized regression spline"> penalized regression spline</a>, <a href="https://publications.waset.org/search?q=locally%20weighted%20regression." title=" locally weighted regression."> locally weighted regression.</a> </p> <a href="https://publications.waset.org/9998959/orthogonal-regression-for-nonparametric-estimation-of-errors-in-variables-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9998959/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9998959/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9998959/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9998959/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9998959/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9998959/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9998959/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9998959/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9998959/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9998959/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9998959.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2133</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">759</span> On the outlier Detection in Nonlinear Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hossein%20Riazoshams">Hossein Riazoshams</a>, <a href="https://publications.waset.org/search?q=Midi%20Habshah"> Midi Habshah</a>, <a href="https://publications.waset.org/search?q=Jr."> Jr.</a>, <a href="https://publications.waset.org/search?q=Mohamad%20Bakri%20Adam"> Mohamad Bakri Adam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The detection of outliers is very essential because of their responsibility for producing huge interpretative problem in linear as well as in nonlinear regression analysis. Much work has been accomplished on the identification of outlier in linear regression, but not in nonlinear regression. In this article we propose several outlier detection techniques for nonlinear regression. The main idea is to use the linear approximation of a nonlinear model and consider the gradient as the design matrix. Subsequently, the detection techniques are formulated. Six detection measures are developed that combined with three estimation techniques such as the Least-Squares, M and MM-estimators. The study shows that among the six measures, only the studentized residual and Cook Distance which combined with the MM estimator, consistently capable of identifying the correct outliers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Nonlinear%20Regression" title="Nonlinear Regression">Nonlinear Regression</a>, <a href="https://publications.waset.org/search?q=outliers" title=" outliers"> outliers</a>, <a href="https://publications.waset.org/search?q=Gradient" title=" Gradient"> Gradient</a>, <a href="https://publications.waset.org/search?q=LeastSquare" title=" LeastSquare"> LeastSquare</a>, <a href="https://publications.waset.org/search?q=M-estimate" title=" M-estimate"> M-estimate</a>, <a href="https://publications.waset.org/search?q=MM-estimate." title=" MM-estimate."> MM-estimate.</a> </p> <a href="https://publications.waset.org/9291/on-the-outlier-detection-in-nonlinear-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9291/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9291/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9291/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9291/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9291/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9291/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9291/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9291/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9291/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9291/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9291.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3179</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">758</span> Robust Regression and its Application in Financial Data Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mansoor%20Momeni">Mansoor Momeni</a>, <a href="https://publications.waset.org/search?q=Mahmoud%20Dehghan%20Nayeri"> Mahmoud Dehghan Nayeri</a>, <a href="https://publications.waset.org/search?q=Ali%20Faal%20Ghayoumi"> Ali Faal Ghayoumi</a>, <a href="https://publications.waset.org/search?q=Hoda%20Ghorbani"> Hoda Ghorbani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from the robust regression and the least square regression shows that the former can provide the possibility of a better and more realistic analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression is recommended for getting more precise results in financial data analysis.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Financial%20data%20analysis" title="Financial data analysis">Financial data analysis</a>, <a href="https://publications.waset.org/search?q=Influential%20data" title=" Influential data"> Influential data</a>, <a href="https://publications.waset.org/search?q=Outliers" title=" Outliers"> Outliers</a>, <a href="https://publications.waset.org/search?q=Robust%20regression." title=" Robust regression."> Robust regression.</a> </p> <a href="https://publications.waset.org/15069/robust-regression-and-its-application-in-financial-data-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15069/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15069/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15069/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15069/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15069/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15069/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15069/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15069/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15069/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15069/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15069.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1932</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">757</span> Regression Test Selection Technique for Multi-Programming Language</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Walid%20S.%20Abd%20El-hamid">Walid S. Abd El-hamid</a>, <a href="https://publications.waset.org/search?q=Sherif%20S.%20El-Etriby"> Sherif S. El-Etriby</a>, <a href="https://publications.waset.org/search?q=Mohiy%20M.%20Hadhoud"> Mohiy M. Hadhoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Regression testing is a maintenance activity applied to modified software to provide confidence that the changed parts are correct and that the unchanged parts have not been adversely affected by the modifications. Regression test selection techniques reduce the cost of regression testing, by selecting a subset of an existing test suite to use in retesting modified programs. This paper presents the first general regression-test-selection technique, which based on code and allows selecting test cases for any programs written in any programming language. Then it handles incomplete program. We also describe RTSDiff, a regression-test-selection system that implements the proposed technique. The results of the empirical studied that performed in four programming languages java, C#, C碌 and Visual basic show that the efficiency and effective in reducing the size of test suit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Regression%20testing" title="Regression testing">Regression testing</a>, <a href="https://publications.waset.org/search?q=testing" title=" testing"> testing</a>, <a href="https://publications.waset.org/search?q=test%20selection" title=" test selection"> test selection</a>, <a href="https://publications.waset.org/search?q=softwareevolution" title=" softwareevolution"> softwareevolution</a>, <a href="https://publications.waset.org/search?q=software%20maintenance." title=" software maintenance."> software maintenance.</a> </p> <a href="https://publications.waset.org/12073/regression-test-selection-technique-for-multi-programming-language" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12073/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12073/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12073/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12073/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12073/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12073/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12073/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12073/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12073/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12073/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12073.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1533</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">756</span> Model-Based Software Regression Test Suite Reduction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Shiwei%20Deng">Shiwei Deng</a>, <a href="https://publications.waset.org/search?q=Yang%20Bao"> Yang Bao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a model-based regression test suite reducing approach that uses EFSM model dependence analysis and probability-driven greedy algorithm to reduce software regression test suites. The approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. The EFSM dependence analysis is performed for each elementary modification to reduce the regression test suite, and then the probability-driven greedy algorithm is adopted to select the minimum set of test cases from the reduced regression test suite that cover all interaction patterns. Our initial experience shows that the approach may significantly reduce the size of regression test suites. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Dependence%20analysis" title="Dependence analysis">Dependence analysis</a>, <a href="https://publications.waset.org/search?q=EFSM%20model" title=" EFSM model"> EFSM model</a>, <a href="https://publications.waset.org/search?q=greedy%0D%0Aalgorithm" title=" greedy algorithm"> greedy algorithm</a>, <a href="https://publications.waset.org/search?q=regression%20test." title=" regression test."> regression test.</a> </p> <a href="https://publications.waset.org/10001782/model-based-software-regression-test-suite-reduction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10001782/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10001782/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10001782/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10001782/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10001782/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10001782/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10001782/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10001782/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10001782/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10001782/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10001782.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1921</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">755</span> Stock Market Prediction by Regression Model with Social Moods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Masahiro%20Ohmura">Masahiro Ohmura</a>, <a href="https://publications.waset.org/search?q=Koh%20Kakusho"> Koh Kakusho</a>, <a href="https://publications.waset.org/search?q=Takeshi%20Okadome"> Takeshi Okadome</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model, where document topics are extracted using LDA. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Regression%20model" title="Regression model">Regression model</a>, <a href="https://publications.waset.org/search?q=social%20mood" title=" social mood"> social mood</a>, <a href="https://publications.waset.org/search?q=stock%20market%0D%0Aprediction" title=" stock market prediction"> stock market prediction</a>, <a href="https://publications.waset.org/search?q=Twitter." title=" Twitter."> Twitter.</a> </p> <a href="https://publications.waset.org/9999432/stock-market-prediction-by-regression-model-with-social-moods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999432/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999432/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999432/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999432/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999432/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999432/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999432/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999432/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999432/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999432/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999432.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2434</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">754</span> A Fuzzy Linear Regression Model Based on Dissemblance Index</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Shih-Pin%20Chen">Shih-Pin Chen</a>, <a href="https://publications.waset.org/search?q=Shih-Syuan%20You"> Shih-Syuan You</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fuzzy regression models are useful for investigating the relationship between explanatory variables and responses in fuzzy environments. To overcome the deficiencies of previous models and increase the explanatory power of fuzzy data, the graded mean integration (GMI) representation is applied to determine representative crisp regression coefficients. A fuzzy regression model is constructed based on the modified dissemblance index (MDI), which can precisely measure the actual total error. Compared with previous studies based on the proposed MDI and distance criterion, the results from commonly used test examples show that the proposed fuzzy linear regression model has higher explanatory power and forecasting accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Dissemblance%20index" title="Dissemblance index">Dissemblance index</a>, <a href="https://publications.waset.org/search?q=fuzzy%20linear%20regression" title=" fuzzy linear regression"> fuzzy linear regression</a>, <a href="https://publications.waset.org/search?q=graded%0D%0Amean%20integration" title=" graded mean integration"> graded mean integration</a>, <a href="https://publications.waset.org/search?q=mathematical%20programming." title=" mathematical programming."> mathematical programming.</a> </p> <a href="https://publications.waset.org/10003945/a-fuzzy-linear-regression-model-based-on-dissemblance-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003945/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10003945/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10003945/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10003945/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10003945/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10003945/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10003945/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10003945/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10003945/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10003945/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10003945.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1442</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">753</span> Segmentation of Piecewise Polynomial Regression Model by Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Piecewise" title="Piecewise">Piecewise</a>, <a href="https://publications.waset.org/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/search?q=reversible%20jump%20MCMC" title=" reversible jump MCMC"> reversible jump MCMC</a>, <a href="https://publications.waset.org/search?q=segmentation." title=" segmentation."> segmentation.</a> </p> <a href="https://publications.waset.org/10004307/segmentation-of-piecewise-polynomial-regression-model-by-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10004307/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10004307/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10004307/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10004307/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10004307/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10004307/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10004307/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10004307/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10004307/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10004307/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10004307.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1668</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">752</span> Fuzzy Logic Approach to Robust Regression Models of Uncertain Medical Categories</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Arkady%20Bolotin">Arkady Bolotin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Dichotomization of the outcome by a single cut-off point is an important part of various medical studies. Usually the relationship between the resulted dichotomized dependent variable and explanatory variables is analyzed with linear regression, probit regression or logistic regression. However, in many real-life situations, a certain cut-off point dividing the outcome into two groups is unknown and can be specified only approximately, i.e. surrounded by some (small) uncertainty. It means that in order to have any practical meaning the regression model must be robust to this uncertainty. In this paper, we show that neither the beta in the linear regression model, nor its significance level is robust to the small variations in the dichotomization cut-off point. As an alternative robust approach to the problem of uncertain medical categories, we propose to use the linear regression model with the fuzzy membership function as a dependent variable. This fuzzy membership function denotes to what degree the value of the underlying (continuous) outcome falls below or above the dichotomization cut-off point. In the paper, we demonstrate that the linear regression model of the fuzzy dependent variable can be insensitive against the uncertainty in the cut-off point location. In the paper we present the modeling results from the real study of low hemoglobin levels in infants. We systematically test the robustness of the binomial regression model and the linear regression model with the fuzzy dependent variable by changing the boundary for the category Anemia and show that the behavior of the latter model persists over a quite wide interval.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Categorization" title="Categorization">Categorization</a>, <a href="https://publications.waset.org/search?q=Uncertain%20medical%20categories" title=" Uncertain medical categories"> Uncertain medical categories</a>, <a href="https://publications.waset.org/search?q=Binomial%20regression%20model" title=" Binomial regression model"> Binomial regression model</a>, <a href="https://publications.waset.org/search?q=Fuzzy%20dependent%20variable" title=" Fuzzy dependent variable"> Fuzzy dependent variable</a>, <a href="https://publications.waset.org/search?q=Robustness." title=" Robustness."> Robustness.</a> </p> <a href="https://publications.waset.org/11889/fuzzy-logic-approach-to-robust-regression-models-of-uncertain-medical-categories" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11889/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11889/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11889/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11889/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11889/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11889/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11889/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11889/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11889/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11889/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11889.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1559</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">751</span> The Relative Efficiency of Parameter Estimation in Linear Weighted Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Baoguang%20Tian">Baoguang Tian</a>, <a href="https://publications.waset.org/search?q=Nan%20Chen"> Nan Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A new relative efficiency in linear model in reference is instructed into the linear weighted regression, and its upper and lower bound are proposed. In the linear weighted regression model, for the best linear unbiased estimation of mean matrix respect to the least-squares estimation, two new relative efficiencies are given, and their upper and lower bounds are also studied.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Linear%20weighted%20regression" title="Linear weighted regression">Linear weighted regression</a>, <a href="https://publications.waset.org/search?q=Relative%20efficiency" title=" Relative efficiency"> Relative efficiency</a>, <a href="https://publications.waset.org/search?q=Mean%20matrix" title=" Mean matrix"> Mean matrix</a>, <a href="https://publications.waset.org/search?q=Trace." title=" Trace."> Trace.</a> </p> <a href="https://publications.waset.org/9999966/the-relative-efficiency-of-parameter-estimation-in-linear-weighted-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999966/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999966/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999966/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999966/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999966/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999966/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999966/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999966/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999966/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999966/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999966.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2473</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">750</span> Internet Purchases in European Union Countries: Multiple Linear Regression Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ksenija%20Dumi%C4%8Di%C4%87">Ksenija Dumi膷i膰</a>, <a href="https://publications.waset.org/search?q=Anita%20%C4%8Ceh%20%C4%8Casni"> Anita 膶eh 膶asni</a>, <a href="https://publications.waset.org/search?q=Irena%20Pali%C4%87"> Irena Pali膰</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper examines economic and Information and Communication Technology (ICT) development influence on recently increasing Internet purchases by individuals for European Union member states. After a growing trend for Internet purchases in EU27 was noticed, all possible regression analysis was applied using nine independent variables in 2011. Finally, two linear regression models were studied in detail. Conducted simple linear regression analysis confirmed the research hypothesis that the Internet purchases in analyzed EU countries is positively correlated with statistically significant variable Gross Domestic Product <em>per capita </em>(GDPpc). Also, analyzed multiple linear regression model with four regressors, showing ICT development level, indicates that ICT development is crucial for explaining the Internet purchases by individuals, confirming the research hypothesis.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=European%20Union" title="European Union">European Union</a>, <a href="https://publications.waset.org/search?q=Internet%20purchases" title=" Internet purchases"> Internet purchases</a>, <a href="https://publications.waset.org/search?q=multiple%20linear%20regression%20model" title=" multiple linear regression model"> multiple linear regression model</a>, <a href="https://publications.waset.org/search?q=outlier" title=" outlier"> outlier</a> </p> <a href="https://publications.waset.org/9998011/internet-purchases-in-european-union-countries-multiple-linear-regression-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9998011/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9998011/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9998011/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9998011/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9998011/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9998011/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9998011/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9998011/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9998011/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9998011/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9998011.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2955</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">749</span> Extended Least Squares LS鈥揝VM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=J%C3%B3zsef%20Valyon">J贸zsef Valyon</a>, <a href="https://publications.waset.org/search?q=G%C3%A1bor%20Horv%C3%A1th"> G谩bor Horv谩th</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they eliminate certain crucial questions involved by neural network construction. The main drawback of standard SVM is its high computational complexity, therefore recently a new technique, the Least Squares SVM (LS鈥揝VM) has been introduced. In this paper we present an extended view of the Least Squares Support Vector Regression (LS鈥揝VR), which enables us to develop new formulations and algorithms to this regression technique. Based on manipulating the linear equation set -which embodies all information about the regression in the learning process- some new methods are introduced to simplify the formulations, speed up the calculations and/or provide better results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Function%20estimation" title="Function estimation">Function estimation</a>, <a href="https://publications.waset.org/search?q=Least%E2%80%93Squares%20Support%20VectorMachines" title=" Least鈥揝quares Support VectorMachines"> Least鈥揝quares Support VectorMachines</a>, <a href="https://publications.waset.org/search?q=Regression" title=" Regression"> Regression</a>, <a href="https://publications.waset.org/search?q=System%20Modeling" title=" System Modeling"> System Modeling</a> </p> <a href="https://publications.waset.org/8869/extended-least-squares-ls-svm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8869/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8869/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8869/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8869/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8869/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8869/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8869/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8869/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8869/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8869/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8869.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2009</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">748</span> Optimization of Slider Crank Mechanism Using Design of Experiments and Multi-Linear Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Galal%20Elkobrosy">Galal Elkobrosy</a>, <a href="https://publications.waset.org/search?q=Amr%20M.%20Abdelrazek"> Amr M. Abdelrazek</a>, <a href="https://publications.waset.org/search?q=Bassuny%20M.%20Elsouhily"> Bassuny M. Elsouhily</a>, <a href="https://publications.waset.org/search?q=Mohamed%20E.%20Khidr"> Mohamed E. Khidr</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Crank shaft length, connecting rod length, crank angle, engine rpm, cylinder bore, mass of piston and compression ratio are the inputs that can control the performance of the slider crank mechanism and then its efficiency. Several combinations of these seven inputs are used and compared. The throughput engine torque predicted by the simulation is analyzed through two different regression models, with and without interaction terms, developed according to multi-linear regression using LU decomposition to solve system of algebraic equations. These models are validated. A regression model in seven inputs including their interaction terms lowered the polynomial degree from 3<sup>rd</sup> degree to 1<sup>st </sup>degree and suggested valid predictions and stable explanations.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Design%20of%20experiments" title="Design of experiments">Design of experiments</a>, <a href="https://publications.waset.org/search?q=regression%20analysis" title=" regression analysis"> regression analysis</a>, <a href="https://publications.waset.org/search?q=SI%20Engine" title=" SI Engine"> SI Engine</a>, <a href="https://publications.waset.org/search?q=statistical%20modeling." title=" statistical modeling."> statistical modeling.</a> </p> <a href="https://publications.waset.org/10008867/optimization-of-slider-crank-mechanism-using-design-of-experiments-and-multi-linear-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10008867/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10008867/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10008867/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10008867/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10008867/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10008867/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10008867/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10008867/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10008867/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10008867/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10008867.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1252</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">747</span> Churn Prediction: Does Technology Matter?</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=John%20Hadden">John Hadden</a>, <a href="https://publications.waset.org/search?q=Ashutosh%20Tiwari"> Ashutosh Tiwari</a>, <a href="https://publications.waset.org/search?q=Rajkumar%20Roy"> Rajkumar Roy</a>, <a href="https://publications.waset.org/search?q=Dymitr%20Ruta"> Dymitr Ruta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Churn" title="Churn">Churn</a>, <a href="https://publications.waset.org/search?q=Decision%20Trees" title=" Decision Trees"> Decision Trees</a>, <a href="https://publications.waset.org/search?q=Neural%20Networks" title=" Neural Networks"> Neural Networks</a>, <a href="https://publications.waset.org/search?q=Regression." title="Regression.">Regression.</a> </p> <a href="https://publications.waset.org/1793/churn-prediction-does-technology-matter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1793/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1793/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1793/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1793/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1793/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1793/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1793/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1793/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1793/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1793/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1793.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3301</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">746</span> Categorical Data Modeling: Logistic Regression Software</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Abdellatif%20Tchantchane">Abdellatif Tchantchane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A Matlab based software for logistic regression is developed to enhance the process of teaching quantitative topics and assist researchers with analyzing wide area of applications where categorical data is involved. The software offers an option of performing stepwise logistic regression to select the most significant predictors. The software includes a feature to detect influential observations in data, and investigates the effect of dropping or misclassifying an observation on a predictor variable. The input data may consist either as a set of individual responses (yes/no) with the predictor variables or as grouped records summarizing various categories for each unique set of predictor variables' values. Graphical displays are used to output various statistical results and to assess the goodness of fit of the logistic regression model. The software recognizes possible convergence constraints when present in data, and the user is notified accordingly.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Logistic%20regression" title="Logistic regression">Logistic regression</a>, <a href="https://publications.waset.org/search?q=Matlab" title=" Matlab"> Matlab</a>, <a href="https://publications.waset.org/search?q=Categorical%20data" title=" Categorical data"> Categorical data</a>, <a href="https://publications.waset.org/search?q=Influential%20observation." title=" Influential observation."> Influential observation.</a> </p> <a href="https://publications.waset.org/11499/categorical-data-modeling-logistic-regression-software" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11499/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11499/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11499/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11499/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11499/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11499/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11499/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11499/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11499/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11499/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11499.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1882</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">745</span> Research on the Problems of Housing Prices in Qingdao from a Macro Perspective</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Liu%20Zhiyuan">Liu Zhiyuan</a>, <a href="https://publications.waset.org/search?q=Sun%20Zongdi"> Sun Zongdi</a>, <a href="https://publications.waset.org/search?q=Liu%20Zhiyuan"> Liu Zhiyuan</a>, <a href="https://publications.waset.org/search?q=Sun%20Zongdi"> Sun Zongdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Qingdao is a seaside city. Taking into account the characteristics of Qingdao, this article established a multiple linear regression model to analyze the impact of macroeconomic factors on housing prices. We used stepwise regression method to make multiple linear regression analysis, and made statistical analysis of F test values and T test values. According to the analysis results, the model is continuously optimized. Finally, this article obtained the multiple linear regression equation and the influencing factors, and the reliability of the model was verified by F test and T test.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Housing%20prices" title="Housing prices">Housing prices</a>, <a href="https://publications.waset.org/search?q=multiple%20linear%20regression%20model" title=" multiple linear regression model"> multiple linear regression model</a>, <a href="https://publications.waset.org/search?q=macroeconomic%20factors" title=" macroeconomic factors"> macroeconomic factors</a>, <a href="https://publications.waset.org/search?q=Qingdao%20City." title=" Qingdao City. "> Qingdao City. </a> </p> <a href="https://publications.waset.org/10005830/research-on-the-problems-of-housing-prices-in-qingdao-from-a-macro-perspective" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10005830/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10005830/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10005830/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10005830/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10005830/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10005830/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10005830/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10005830/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10005830/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10005830/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10005830.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1179</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">744</span> Estimating Regression Parameters in Linear Regression Model with a Censored Response Variable</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jesus%20Orbe">Jesus Orbe</a>, <a href="https://publications.waset.org/search?q=Vicente%20Nunez-Anton"> Vicente Nunez-Anton</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this work we study the effect of several covariates X on a censored response variable T with unknown probability distribution. In this context, most of the studies in the literature can be located in two possible general classes of regression models: models that study the effect the covariates have on the hazard function; and models that study the effect the covariates have on the censored response variable. Proposals in this paper are in the second class of models and, more specifically, on least squares based model approach. Thus, using the bootstrap estimate of the bias, we try to improve the estimation of the regression parameters by reducing their bias, for small sample sizes. Simulation results presented in the paper show that, for reasonable sample sizes and censoring levels, the bias is always smaller for the new proposals.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Censored%20response%20variable" title="Censored response variable">Censored response variable</a>, <a href="https://publications.waset.org/search?q=regression" title=" regression"> regression</a>, <a href="https://publications.waset.org/search?q=bias." title=" bias."> bias.</a> </p> <a href="https://publications.waset.org/13785/estimating-regression-parameters-in-linear-regression-model-with-a-censored-response-variable" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13785/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13785/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13785/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13785/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13785/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13785/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13785/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13785/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13785/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13785/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13785.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1475</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">743</span> Adjusted Ratio and Regression Type Estimators for Estimation of Population Mean when some Observations are missing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nuanpan%20Nangsue">Nuanpan Nangsue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Ratio and regression type estimators have been used by previous authors to estimate a population mean for the principal variable from samples in which both auxiliary x and principal y variable data are available. However, missing data are a common problem in statistical analyses with real data. Ratio and regression type estimators have also been used for imputing values of missing y data. In this paper, six new ratio and regression type estimators are proposed for imputing values for any missing y data and estimating a population mean for y from samples with missing x and/or y data. A simulation study has been conducted to compare the six ratio and regression type estimators with a previous estimator of Rueda. Two population sizes N = 1,000 and 5,000 have been considered with sample sizes of 10% and 30% and with correlation coefficients between population variables X and Y of 0.5 and 0.8. In the simulations, 10 and 40 percent of sample y values and 10 and 40 percent of sample x values were randomly designated as missing. The new ratio and regression type estimators give similar mean absolute percentage errors that are smaller than the Rueda estimator for all cases. The new estimators give a large reduction in errors for the case of 40% missing y values and sampling fraction of 30%.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Auxiliary%20variable" title="Auxiliary variable">Auxiliary variable</a>, <a href="https://publications.waset.org/search?q=missing%20data" title=" missing data"> missing data</a>, <a href="https://publications.waset.org/search?q=ratio%20and%20regression%0D%0Atype%20estimators." title=" ratio and regression type estimators."> ratio and regression type estimators.</a> </p> <a href="https://publications.waset.org/14354/adjusted-ratio-and-regression-type-estimators-for-estimation-of-population-mean-when-some-observations-are-missing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14354/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14354/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14354/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14354/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14354/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14354/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14354/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14354/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14354/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14354/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14354.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1732</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">742</span> Quality of Service Evaluation using a Combination of Fuzzy C-Means and Regression Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Aboagela%20Dogman">Aboagela Dogman</a>, <a href="https://publications.waset.org/search?q=Reza%20Saatchi"> Reza Saatchi</a>, <a href="https://publications.waset.org/search?q=Samir%20Al-Khayatt"> Samir Al-Khayatt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a network quality of service (QoS) evaluation system was proposed. The system used a combination of fuzzy C-means (FCM) and regression model to analyse and assess the QoS in a simulated network. Network QoS parameters of multimedia applications were intelligently analysed by FCM clustering algorithm. The QoS parameters for each FCM cluster centre were then inputted to a regression model in order to quantify the overall QoS. The proposed QoS evaluation system provided valuable information about the network-s QoS patterns and based on this information, the overall network-s QoS was effectively quantified. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Fuzzy%20C-means%3B%20regression%20model" title="Fuzzy C-means; regression model">Fuzzy C-means; regression model</a>, <a href="https://publications.waset.org/search?q=network%20quality%0Aof%20service" title=" network quality of service"> network quality of service</a> </p> <a href="https://publications.waset.org/12298/quality-of-service-evaluation-using-a-combination-of-fuzzy-c-means-and-regression-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/12298/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/12298/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/12298/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/12298/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/12298/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/12298/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/12298/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/12298/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/12298/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/12298/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/12298.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1720</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">741</span> Defect Cause Modeling with Decision Tree and Regression Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=B.%20Bak%C4%B1r">B. Bak谋r</a>, <a href="https://publications.waset.org/search?q=%C4%B0.%20Batmaz"> 陌. Batmaz</a>, <a href="https://publications.waset.org/search?q=F.%20A.%20G%C3%BCnt%C3%BCrk%C3%BCn"> F. A. G眉nt眉rk眉n</a>, <a href="https://publications.waset.org/search?q=%C4%B0.%20A.%20%C4%B0pek%C3%A7i"> 陌. A. 陌pek莽i</a>, <a href="https://publications.waset.org/search?q=G.%20K%C3%B6ksal"> G. K枚ksal</a>, <a href="https://publications.waset.org/search?q=N.%20E.%20%C3%96zdemirel"> N. E. 脰zdemirel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main aim of this study is to identify the most influential variables that cause defects on the items produced by a casting company located in Turkey. To this end, one of the items produced by the company with high defective percentage rates is selected. Two approaches-the regression analysis and decision treesare used to model the relationship between process parameters and defect types. Although logistic regression models failed, decision tree model gives meaningful results. Based on these results, it can be claimed that the decision tree approach is a promising technique for determining the most important process variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Casting%20industry" title="Casting industry">Casting industry</a>, <a href="https://publications.waset.org/search?q=decision%20tree%20algorithm%20C5.0" title=" decision tree algorithm C5.0"> decision tree algorithm C5.0</a>, <a href="https://publications.waset.org/search?q=logistic%20regression" title="logistic regression">logistic regression</a>, <a href="https://publications.waset.org/search?q=quality%20improvement." title=" quality improvement."> quality improvement.</a> </p> <a href="https://publications.waset.org/9221/defect-cause-modeling-with-decision-tree-and-regression-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9221/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9221/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9221/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9221/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9221/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9221/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9221/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9221/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9221/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9221/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9221.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2519</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">740</span> Performance Analysis of Adaptive LMS Filter through Regression Analysis using SystemC</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hyeong-Geon%20Lee">Hyeong-Geon Lee</a>, <a href="https://publications.waset.org/search?q=Jae-Young%20Park"> Jae-Young Park</a>, <a href="https://publications.waset.org/search?q=Suk-ki%20Lee"> Suk-ki Lee</a>, <a href="https://publications.waset.org/search?q=Jong-Tae%20Kim"> Jong-Tae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The LMS adaptive filter has several parameters which can affect their performance. From among these parameters, most papers handle the step size parameter for controlling the performance. In this paper, we approach three parameters: step-size, filter tap-size and filter form. The regression analysis is used for defining the relation between parameters and performance of LMS adaptive filter with using the system level simulation results. The results present that all parameters have performance trends in each own particular form, which can be estimated from equations drawn by regression analysis.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=System%20level%20model" title="System level model">System level model</a>, <a href="https://publications.waset.org/search?q=adaptive%20LMS%20FIR%20filter" title=" adaptive LMS FIR filter"> adaptive LMS FIR filter</a>, <a href="https://publications.waset.org/search?q=regression%20analysis" title=" regression analysis"> regression analysis</a>, <a href="https://publications.waset.org/search?q=systemC." title=" systemC."> systemC.</a> </p> <a href="https://publications.waset.org/13321/performance-analysis-of-adaptive-lms-filter-through-regression-analysis-using-systemc" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13321/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13321/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13321/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13321/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13321/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13321/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13321/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13321/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13321/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13321/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13321.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2800</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">739</span> Density Estimation using Generalized Linear Model and a Linear Combination of Gaussians </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Aly%20Farag">Aly Farag</a>, <a href="https://publications.waset.org/search?q=Ayman%20El-Baz"> Ayman El-Baz</a>, <a href="https://publications.waset.org/search?q=Refaat%20Mohamed"> Refaat Mohamed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Logistic%20regression%20model" title="Logistic regression model">Logistic regression model</a>, <a href="https://publications.waset.org/search?q=Expectationmaximization" title=" Expectationmaximization"> Expectationmaximization</a>, <a href="https://publications.waset.org/search?q=Segmentation." title=" Segmentation."> Segmentation.</a> </p> <a href="https://publications.waset.org/10994/density-estimation-using-generalized-linear-model-and-a-linear-combination-of-gaussians" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10994/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10994/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10994/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10994/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10994/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10994/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10994/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10994/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10994/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10994/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10994.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1733</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">738</span> Multiple Regression based Graphical Modeling for Images </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Pavan%20S.">Pavan S.</a>, <a href="https://publications.waset.org/search?q=Sridhar%20G."> Sridhar G.</a>, <a href="https://publications.waset.org/search?q=Sridhar%20V."> Sridhar V.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Super resolution is one of the commonly referred inference problems in computer vision. In the case of images, this problem is generally addressed using a graphical model framework wherein each node represents a portion of the image and the edges between the nodes represent the statistical dependencies. However, the large dimensionality of images along with the large number of possible states for a node makes the inference problem computationally intractable. In this paper, we propose a representation wherein each node can be represented as acombination of multiple regression functions. The proposed approach achieves a tradeoff between the computational complexity and inference accuracy by varying the number of regression functions for a node.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Belief%20propagation" title="Belief propagation">Belief propagation</a>, <a href="https://publications.waset.org/search?q=Graphical%20model" title=" Graphical model"> Graphical model</a>, <a href="https://publications.waset.org/search?q=Regression" title=" Regression"> Regression</a>, <a href="https://publications.waset.org/search?q=Super%20resolution." title="Super resolution.">Super resolution.</a> </p> <a href="https://publications.waset.org/2586/multiple-regression-based-graphical-modeling-for-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/2586/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/2586/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/2586/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/2586/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/2586/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/2586/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/2586/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/2586/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/2586/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/2586/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/2586.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1547</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">737</span> Empirical Statistical Modeling of Rainfall Prediction over Myanmar</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Wint%20Thida%20Zaw">Wint Thida Zaw</a>, <a href="https://publications.waset.org/search?q=Thinn%20Thu%20Naing"> Thinn Thu Naing</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the essential sectors of Myanmar economy is agriculture which is sensitive to climate variation. The most important climatic element which impacts on agriculture sector is rainfall. Thus rainfall prediction becomes an important issue in agriculture country. Multi variables polynomial regression (MPR) provides an effective way to describe complex nonlinear input output relationships so that an outcome variable can be predicted from the other or others. In this paper, the modeling of monthly rainfall prediction over Myanmar is described in detail by applying the polynomial regression equation. The proposed model results are compared to the results produced by multiple linear regression model (MLR). Experiments indicate that the prediction model based on MPR has higher accuracy than using MLR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Polynomial%20Regression" title="Polynomial Regression">Polynomial Regression</a>, <a href="https://publications.waset.org/search?q=Rainfall%20Forecasting" title=" Rainfall Forecasting"> Rainfall Forecasting</a>, <a href="https://publications.waset.org/search?q=Statistical%20forecasting." title="Statistical forecasting.">Statistical forecasting.</a> </p> <a href="https://publications.waset.org/15031/empirical-statistical-modeling-of-rainfall-prediction-over-myanmar" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15031/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15031/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15031/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15031/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15031/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15031/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15031/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15031/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15031/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15031/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15031.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2634</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">736</span> Estimation of Time -Varying Linear Regression with Unknown Time -Volatility via Continuous Generalization of the Akaike Information Criterion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Elena%20Ezhova">Elena Ezhova</a>, <a href="https://publications.waset.org/search?q=Vadim%20Mottl"> Vadim Mottl</a>, <a href="https://publications.waset.org/search?q=Olga%20Krasotkina"> Olga Krasotkina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The problem of estimating time-varying regression is inevitably concerned with the necessity to choose the appropriate level of model volatility - ranging from the full stationarity of instant regression models to their absolute independence of each other. In the stationary case the number of regression coefficients to be estimated equals that of regressors, whereas the absence of any smoothness assumptions augments the dimension of the unknown vector by the factor of the time-series length. The Akaike Information Criterion is a commonly adopted means of adjusting a model to the given data set within a succession of nested parametric model classes, but its crucial restriction is that the classes are rigidly defined by the growing integer-valued dimension of the unknown vector. To make the Kullback information maximization principle underlying the classical AIC applicable to the problem of time-varying regression estimation, we extend it onto a wider class of data models in which the dimension of the parameter is fixed, but the freedom of its values is softly constrained by a family of continuously nested a priori probability distributions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Time%20varying%20regression" title="Time varying regression">Time varying regression</a>, <a href="https://publications.waset.org/search?q=time-volatility%20of%20regression%0Acoefficients" title=" time-volatility of regression coefficients"> time-volatility of regression coefficients</a>, <a href="https://publications.waset.org/search?q=Akaike%20Information%20Criterion%20%28AIC%29" title=" Akaike Information Criterion (AIC)"> Akaike Information Criterion (AIC)</a>, <a href="https://publications.waset.org/search?q=Kullback%20information%0Amaximization%20principle." title=" Kullback information maximization principle."> Kullback information maximization principle.</a> </p> <a href="https://publications.waset.org/3083/estimation-of-time-varying-linear-regression-with-unknown-time-volatility-via-continuous-generalization-of-the-akaike-information-criterion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3083/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3083/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3083/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3083/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3083/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3083/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3083/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3083/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3083/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3083/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3083.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1534</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">735</span> Comparison of Neural Network and Logistic Regression Methods to Predict Xerostomia after Radiotherapy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hui-Min%20Ting">Hui-Min Ting</a>, <a href="https://publications.waset.org/search?q=Tsair-Fwu%20Lee"> Tsair-Fwu Lee</a>, <a href="https://publications.waset.org/search?q=Ming-Yuan%20Cho"> Ming-Yuan Cho</a>, <a href="https://publications.waset.org/search?q=Pei-Ju%20Chao"> Pei-Ju Chao</a>, <a href="https://publications.waset.org/search?q=Chun-Ming%20Chang"> Chun-Ming Chang</a>, <a href="https://publications.waset.org/search?q=Long-Chang%20Chen"> Long-Chang Chen</a>, <a href="https://publications.waset.org/search?q=Fu-Min%20Fang"> Fu-Min Fang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>To evaluate the ability to predict xerostomia after radiotherapy, we constructed and compared neural network and logistic regression models. In this study, 61 patients who completed a questionnaire about their quality of life (QoL) before and after a full course of radiation therapy were included. Based on this questionnaire, some statistical data about the condition of the patients’ salivary glands were obtained, and these subjects were included as the inputs of the neural network and logistic regression models in order to predict the probability of xerostomia. Seven variables were then selected from the statistical data according to Cramer’s V and point-biserial correlation values and were trained by each model to obtain the respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65 for SSE, and 13.7% and 19.0% for MAPE, respectively. These parameters demonstrate that both neural network and logistic regression methods are effective for predicting conditions of parotid glands.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=NPC" title="NPC">NPC</a>, <a href="https://publications.waset.org/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/search?q=xerostomia." title=" xerostomia."> xerostomia.</a> </p> <a href="https://publications.waset.org/16509/comparison-of-neural-network-and-logistic-regression-methods-to-predict-xerostomia-after-radiotherapy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16509/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16509/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16509/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16509/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16509/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16509/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16509/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16509/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16509/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16509/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16509.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1636</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">734</span> Bioprocess Optimization Based On Relevance Vector Regression Models and Evolutionary Programming Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=R.%20Simutis">R. Simutis</a>, <a href="https://publications.waset.org/search?q=V.%20Galvanauskas"> V. Galvanauskas</a>, <a href="https://publications.waset.org/search?q=D.%20Levisauskas"> D. Levisauskas</a>, <a href="https://publications.waset.org/search?q=J.%20Repsyte"> J. Repsyte</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper proposes a bioprocess optimization procedure based on Relevance Vector Regression models and evolutionary programming technique. Relevance Vector Regression scheme allows developing a compact and stable data-based process model avoiding time-consuming modeling expenses. The model building and process optimization procedure could be done in a half-automated way and repeated after every new cultivation run. The proposed technique was tested in a simulated mammalian cell cultivation process. The obtained results are promising and could be attractive for optimization of industrial bioprocesses.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Bioprocess%20optimization" title="Bioprocess optimization">Bioprocess optimization</a>, <a href="https://publications.waset.org/search?q=Evolutionary%0D%0Aprogramming" title=" Evolutionary programming"> Evolutionary programming</a>, <a href="https://publications.waset.org/search?q=Relevance%20Vector%20Regression." title=" Relevance Vector Regression."> Relevance Vector Regression.</a> </p> <a href="https://publications.waset.org/9998131/bioprocess-optimization-based-on-relevance-vector-regression-models-and-evolutionary-programming-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9998131/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9998131/bibtex" target="_blank" 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