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regression</title> <meta name="description" content="Search results for: penalized regression"> <meta name="keywords" content="penalized 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 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="penalized 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> 3239</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: penalized regression</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3239</span> A Comparison of Smoothing Spline Method and Penalized Spline Regression Method Based on Nonparametric Regression Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Autcha%20Araveeporn">Autcha Araveeporn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a study about a nonparametric regression model consisting of a smoothing spline method and a penalized spline regression method. We also compare the techniques used for estimation and prediction of nonparametric regression model. We tried both methods with crude oil prices in dollars per barrel and the Stock Exchange of Thailand (SET) index. According to the results, it is concluded that smoothing spline method performs better than that of penalized spline regression method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20regression%20model" title="nonparametric regression model">nonparametric regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20spline%20regression%20method" title=" penalized spline regression method"> penalized spline regression method</a>, <a href="https://publications.waset.org/abstracts/search?q=smoothing%20spline%20method" title=" smoothing spline method"> smoothing spline method</a>, <a href="https://publications.waset.org/abstracts/search?q=Stock%20Exchange%20of%20Thailand%20%28SET%29" title=" Stock Exchange of Thailand (SET)"> Stock Exchange of Thailand (SET)</a> </p> <a href="https://publications.waset.org/abstracts/2974/a-comparison-of-smoothing-spline-method-and-penalized-spline-regression-method-based-on-nonparametric-regression-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2974.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">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3238</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/abstracts/search?q=Anastasiia%20Yu.%20Timofeeva">Anastasiia Yu. Timofeeva</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=grade%20point%20average" title="grade point average">grade point average</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20regression" title=" orthogonal regression"> orthogonal regression</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20regression%20spline" title=" penalized regression spline"> penalized regression spline</a>, <a href="https://publications.waset.org/abstracts/search?q=locally%20weighted%20regression" title=" locally weighted regression"> locally weighted regression</a> </p> <a href="https://publications.waset.org/abstracts/11927/orthogonal-regression-for-nonparametric-estimation-of-errors-in-variables-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11927.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">416</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3237</span> Credit Risk Prediction Based on Bayesian Estimation of Logistic Regression Model with Random Effects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sami%20Mestiri">Sami Mestiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdeljelil%20Farhat"> Abdeljelil Farhat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this current paper is to predict the credit risk of banks in Tunisia, over the period (2000-2005). For this purpose, two methods for the estimation of the logistic regression model with random effects: Penalized Quasi Likelihood (PQL) method and Gibbs Sampler algorithm are applied. By using the information on a sample of 528 Tunisian firms and 26 financial ratios, we show that Bayesian approach improves the quality of model predictions in terms of good classification as well as by the ROC curve result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting" title="forecasting">forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20risk" title=" credit risk"> credit risk</a>, <a href="https://publications.waset.org/abstracts/search?q=Penalized%20Quasi%20Likelihood" title=" Penalized Quasi Likelihood"> Penalized Quasi Likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=Gibbs%20Sampler" title=" Gibbs Sampler"> Gibbs Sampler</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression%20with%20random%20effects" title=" logistic regression with random effects"> logistic regression with random effects</a>, <a href="https://publications.waset.org/abstracts/search?q=curve%20ROC" title=" curve ROC"> curve ROC</a> </p> <a href="https://publications.waset.org/abstracts/28981/credit-risk-prediction-based-on-bayesian-estimation-of-logistic-regression-model-with-random-effects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28981.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">542</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3236</span> Weighted Rank Regression with Adaptive Penalty Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kang-Mo%20Jung">Kang-Mo Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of regularization for statistical methods has become popular. The least absolute shrinkage and selection operator (LASSO) framework has become the standard tool for sparse regression. However, it is well known that the LASSO is sensitive to outliers or leverage points. We consider a new robust estimation which is composed of the weighted loss function of the pairwise difference of residuals and the adaptive penalty function regulating the tuning parameter for each variable. Rank regression is resistant to regression outliers, but not to leverage points. By adopting a weighted loss function, the proposed method is robust to leverage points of the predictor variable. Furthermore, the adaptive penalty function gives us good statistical properties in variable selection such as oracle property and consistency. We develop an efficient algorithm to compute the proposed estimator using basic functions in program R. We used an optimal tuning parameter based on the Bayesian information criterion (BIC). Numerical simulation shows that the proposed estimator is effective for analyzing real data set and contaminated data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20penalty%20function" title="adaptive penalty function">adaptive penalty function</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20penalized%20regression" title=" robust penalized regression"> robust penalized regression</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20selection" title=" variable selection"> variable selection</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20rank%20regression" title=" weighted rank regression"> weighted rank regression</a> </p> <a href="https://publications.waset.org/abstracts/79449/weighted-rank-regression-with-adaptive-penalty-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79449.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">475</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3235</span> Support Vector Regression with Weighted Least Absolute Deviations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kang-Mo%20Jung">Kang-Mo Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Least squares support vector machine (LS-SVM) is a penalized regression which considers both fitting and generalization ability of a model. However, the squared loss function is very sensitive to even single outlier. We proposed a weighted absolute deviation loss function for the robustness of the estimates in least absolute deviation support vector machine. The proposed estimates can be obtained by a quadratic programming algorithm. Numerical experiments on simulated datasets show that the proposed algorithm is competitive in view of robustness to outliers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=least%20absolute%20deviation" title="least absolute deviation">least absolute deviation</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20programming" title=" quadratic programming"> quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=weight" title=" weight"> weight</a> </p> <a href="https://publications.waset.org/abstracts/23674/support-vector-regression-with-weighted-least-absolute-deviations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23674.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">527</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3234</span> Ground Motion Modeling Using the Least Absolute Shrinkage and Selection Operator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yildiz%20Stella%20Dak">Yildiz Stella Dak</a>, <a href="https://publications.waset.org/abstracts/search?q=Jale%20Tezcan"> Jale Tezcan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ground motion models that relate a strong motion parameter of interest to a set of predictive seismological variables describing the earthquake source, the propagation path of the seismic wave, and the local site conditions constitute a critical component of seismic hazard analyses. When a sufficient number of strong motion records are available, ground motion relations are developed using statistical analysis of the recorded ground motion data. In regions lacking a sufficient number of recordings, a synthetic database is developed using stochastic, theoretical or hybrid approaches. Regardless of the manner the database was developed, ground motion relations are developed using regression analysis. Development of a ground motion relation is a challenging process which inevitably requires the modeler to make subjective decisions regarding the inclusion criteria of the recordings, the functional form of the model and the set of seismological variables to be included in the model. Because these decisions are critically important to the validity and the applicability of the model, there is a continuous interest on procedures that will facilitate the development of ground motion models. This paper proposes the use of the Least Absolute Shrinkage and Selection Operator (LASSO) in selecting the set predictive seismological variables to be used in developing a ground motion relation. The LASSO can be described as a penalized regression technique with a built-in capability of variable selection. Similar to the ridge regression, the LASSO is based on the idea of shrinking the regression coefficients to reduce the variance of the model. Unlike ridge regression, where the coefficients are shrunk but never set equal to zero, the LASSO sets some of the coefficients exactly to zero, effectively performing variable selection. Given a set of candidate input variables and the output variable of interest, LASSO allows ranking the input variables in terms of their relative importance, thereby facilitating the selection of the set of variables to be included in the model. Because the risk of overfitting increases as the ratio of the number of predictors to the number of recordings increases, selection of a compact set of variables is important in cases where a small number of recordings are available. In addition, identification of a small set of variables can improve the interpretability of the resulting model, especially when there is a large number of candidate predictors. A practical application of the proposed approach is presented, using more than 600 recordings from the National Geospatial-Intelligence Agency (NGA) database, where the effect of a set of seismological predictors on the 5% damped maximum direction spectral acceleration is investigated. The set of candidate predictors considered are Magnitude, Rrup, Vs30. Using LASSO, the relative importance of the candidate predictors has been ranked. Regression models with increasing levels of complexity were constructed using one, two, three, and four best predictors, and the models’ ability to explain the observed variance in the target variable have been compared. The bias-variance trade-off in the context of model selection is discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ground%20motion%20modeling" title="ground motion modeling">ground motion modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20absolute%20shrinkage%20and%20selection%20operator" title=" least absolute shrinkage and selection operator"> least absolute shrinkage and selection operator</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20regression" title=" penalized regression"> penalized regression</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20selection" title=" variable selection"> variable selection</a> </p> <a href="https://publications.waset.org/abstracts/88703/ground-motion-modeling-using-the-least-absolute-shrinkage-and-selection-operator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88703.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">330</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3233</span> Behind Fuzzy Regression Approach: An Exploration Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lavinia%20B.%20Dulla">Lavinia B. Dulla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The exploration study of the fuzzy regression approach attempts to present that fuzzy regression can be used as a possible alternative to classical regression. It likewise seeks to assess the differences and characteristics of simple linear regression and fuzzy regression using the width of prediction interval, mean absolute deviation, and variance of residuals. Based on the simple linear regression model, the fuzzy regression approach is worth considering as an alternative to simple linear regression when the sample size is between 10 and 20. As the sample size increases, the fuzzy regression approach is not applicable to use since the assumption regarding large sample size is already operating within the framework of simple linear regression. Nonetheless, it can be suggested for a practical alternative when decisions often have to be made on the basis of small data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20regression%20approach" title="fuzzy regression approach">fuzzy regression approach</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20fuzziness%20criterion" title=" minimum fuzziness criterion"> minimum fuzziness criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20regression" title=" interval regression"> interval regression</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20interval" title=" prediction interval"> prediction interval</a> </p> <a href="https://publications.waset.org/abstracts/139364/behind-fuzzy-regression-approach-an-exploration-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139364.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">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3232</span> Optimization of Machine Learning Regression Results: An Application on Health Expenditures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Songul%20Cinaroglu">Songul Cinaroglu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning regression methods are recommended as an alternative to classical regression methods in the existence of variables which are difficult to model. Data for health expenditure is typically non-normal and have a heavily skewed distribution. This study aims to compare machine learning regression methods by hyperparameter tuning to predict health expenditure per capita. A multiple regression model was conducted and performance results of Lasso Regression, Random Forest Regression and Support Vector Machine Regression recorded when different hyperparameters are assigned. Lambda (λ) value for Lasso Regression, number of trees for Random Forest Regression, epsilon (ε) value for Support Vector Regression was determined as hyperparameters. Study results performed by using 'k' fold cross validation changed from 5 to 50, indicate the difference between machine learning regression results in terms of R², RMSE and MAE values that are statistically significant (p < 0.001). Study results reveal that Random Forest Regression (R² ˃ 0.7500, RMSE ≤ 0.6000 ve MAE ≤ 0.4000) outperforms other machine learning regression methods. It is highly advisable to use machine learning regression methods for modelling health expenditures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=lasso%20regression" title=" lasso regression"> lasso regression</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20regression" title=" random forest regression"> random forest regression</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression" title=" support vector regression"> support vector regression</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperparameter%20tuning" title=" hyperparameter tuning"> hyperparameter tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20expenditure" title=" health expenditure"> health expenditure</a> </p> <a href="https://publications.waset.org/abstracts/97629/optimization-of-machine-learning-regression-results-an-application-on-health-expenditures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97629.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">226</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3231</span> Multidimensional Item Response Theory Models for Practical Application in Large Tests Designed to Measure Multiple Constructs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20Fernanda%20Ordo%C3%B1ez%20Martinez">Maria Fernanda Ordoñez Martinez</a>, <a href="https://publications.waset.org/abstracts/search?q=Alvaro%20Mauricio%20Montenegro"> Alvaro Mauricio Montenegro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20analysis" title="semantic analysis">semantic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=factorial%20analysis" title=" factorial analysis"> factorial analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=dimension%20reduction" title=" dimension reduction"> dimension reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20logistic%20regression" title=" penalized logistic regression"> penalized logistic regression</a> </p> <a href="https://publications.waset.org/abstracts/42128/multidimensional-item-response-theory-models-for-practical-application-in-large-tests-designed-to-measure-multiple-constructs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42128.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">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3230</span> Integrating Process Planning, WMS Dispatching, and WPPW Weighted Due Date Assignment Using a Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Halil%20Ibrahim%20Demir">Halil Ibrahim Demir</a>, <a href="https://publications.waset.org/abstracts/search?q=Tar%C4%B1k%20Cakar"> Tarık Cakar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20Cil"> Ibrahim Cil</a>, <a href="https://publications.waset.org/abstracts/search?q=Muharrem%20Dugenci"> Muharrem Dugenci</a>, <a href="https://publications.waset.org/abstracts/search?q=Caner%20Erden"> Caner Erden</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conventionally, process planning, scheduling, and due-date assignment functions are performed separately and sequentially. The interdependence of these functions requires integration. Although integrated process planning and scheduling, and scheduling with due date assignment problems are popular research topics, only a few works address the integration of these three functions. This work focuses on the integration of process planning, WMS scheduling, and WPPW due date assignment. Another novelty of this work is the use of a weighted due date assignment. In the literature, due dates are generally assigned without considering the importance of customers. However, in this study, more important customers get closer due dates. Typically, only tardiness is punished, but the JIT philosophy punishes both earliness and tardiness. In this study, all weighted earliness, tardiness, and due date related costs are penalized. As no customer desires distant due dates, such distant due dates should be penalized. In this study, various levels of integration of these three functions are tested and genetic search and random search are compared both with each other and with ordinary solutions. Higher integration levels are superior, while search is always useful. Genetic searches outperformed random searches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20planning" title="process planning">process planning</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20scheduling" title=" weighted scheduling"> weighted scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20due-date%20assignment" title=" weighted due-date assignment"> weighted due-date assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20search" title=" random search"> random search</a> </p> <a href="https://publications.waset.org/abstracts/51544/integrating-process-planning-wms-dispatching-and-wppw-weighted-due-date-assignment-using-a-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51544.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">394</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3229</span> A Learning-Based EM Mixture Regression Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Cheng%20Tian">Yi-Cheng Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Miin-Shen%20Yang"> Miin-Shen Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The mixture likelihood approach to clustering is a popular clustering method where the expectation and maximization (EM) algorithm is the most used mixture likelihood method. In the literature, the EM algorithm had been used for mixture regression models. However, these EM mixture regression algorithms are sensitive to initial values with a priori number of clusters. In this paper, to resolve these drawbacks, we construct a learning-based schema for the EM mixture regression algorithm such that it is free of initializations and can automatically obtain an approximately optimal number of clusters. Some numerical examples and comparisons demonstrate the superiority and usefulness of the proposed learning-based EM mixture regression algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=EM%20algorithm" title=" EM algorithm"> EM algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20mixture%20model" title=" Gaussian mixture model"> Gaussian mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=mixture%20regression%20model" title=" mixture regression model"> mixture regression model</a> </p> <a href="https://publications.waset.org/abstracts/25163/a-learning-based-em-mixture-regression-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25163.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">510</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3228</span> Prediction of Energy Storage Areas for Static Photovoltaic System Using Irradiation and Regression Modelling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kisan%20Sarda">Kisan Sarda</a>, <a href="https://publications.waset.org/abstracts/search?q=Bhavika%20Shingote"> Bhavika Shingote</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to evaluate regression modelling for prediction of Energy storage of solar photovoltaic (PV) system using Semi parametric regression techniques because there are some parameters which are known while there are some unknown parameters like humidity, dust etc. Here irradiation of solar energy is different for different places on the basis of Latitudes, so by finding out areas which give more storage we can implement PV systems at those places and our need of energy will be fulfilled. This regression modelling is done for daily, monthly and seasonal prediction of solar energy storage. In this, we have used R modules for designing the algorithm. This algorithm will give the best comparative results than other regression models for the solar PV cell energy storage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semi%20parametric%20regression" title="semi parametric regression">semi parametric regression</a>, <a href="https://publications.waset.org/abstracts/search?q=photovoltaic%20%28PV%29%20system" title=" photovoltaic (PV) system"> photovoltaic (PV) system</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20modelling" title=" regression modelling"> regression modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=irradiation" title=" irradiation"> irradiation</a> </p> <a href="https://publications.waset.org/abstracts/65373/prediction-of-energy-storage-areas-for-static-photovoltaic-system-using-irradiation-and-regression-modelling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65373.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">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3227</span> New Segmentation of Piecewise Linear Regression Models Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation of piecewise linear regression models. The method used to estimate the parameters of picewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of picewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=regression" title="regression">regression</a>, <a href="https://publications.waset.org/abstracts/search?q=piecewise" title=" piecewise"> piecewise</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20Jump%20MCMC" title=" reversible Jump MCMC"> reversible Jump MCMC</a> </p> <a href="https://publications.waset.org/abstracts/31651/new-segmentation-of-piecewise-linear-regression-models-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31651.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">521</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3226</span> Comparison of Methods of Estimation for Use in Goodness of Fit Tests for Binary Multilevel Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20V.%20Pinto">I. V. Pinto</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Sooriyarachchi"> M. R. Sooriyarachchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It can be frequently observed that the data arising in our environment have a hierarchical or a nested structure attached with the data. Multilevel modelling is a modern approach to handle this kind of data. When multilevel modelling is combined with a binary response, the estimation methods get complex in nature and the usual techniques are derived from quasi-likelihood method. The estimation methods which are compared in this study are, marginal quasi-likelihood (order 1 & order 2) (MQL1, MQL2) and penalized quasi-likelihood (order 1 & order 2) (PQL1, PQL2). A statistical model is of no use if it does not reflect the given dataset. Therefore, checking the adequacy of the fitted model through a goodness-of-fit (GOF) test is an essential stage in any modelling procedure. However, prior to usage, it is also equally important to confirm that the GOF test performs well and is suitable for the given model. This study assesses the suitability of the GOF test developed for binary response multilevel models with respect to the method used in model estimation. An extensive set of simulations was conducted using MLwiN (v 2.19) with varying number of clusters, cluster sizes and intra cluster correlations. The test maintained the desirable Type-I error for models estimated using PQL2 and it failed for almost all the combinations of MQL. Power of the test was adequate for most of the combinations in all estimation methods except MQL1. Moreover, models were fitted using the four methods to a real-life dataset and performance of the test was compared for each model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=goodness-of-fit%20test" title="goodness-of-fit test">goodness-of-fit test</a>, <a href="https://publications.waset.org/abstracts/search?q=marginal%20quasi-likelihood" title=" marginal quasi-likelihood"> marginal quasi-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel%20modelling" title=" multilevel modelling"> multilevel modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20quasi-likelihood" title=" penalized quasi-likelihood"> penalized quasi-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=power" title=" power"> power</a>, <a href="https://publications.waset.org/abstracts/search?q=quasi-likelihood" title=" quasi-likelihood"> quasi-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=type-I%20error" title=" type-I error"> type-I error</a> </p> <a href="https://publications.waset.org/abstracts/105519/comparison-of-methods-of-estimation-for-use-in-goodness-of-fit-tests-for-binary-multilevel-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105519.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">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3225</span> Application Difference between Cox and Logistic Regression Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Idrissa%20Kayijuka">Idrissa Kayijuka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The logistic regression and Cox regression models (proportional hazard model) at present are being employed in the analysis of prospective epidemiologic research looking into risk factors in their application on chronic diseases. However, a theoretical relationship between the two models has been studied. By definition, Cox regression model also called Cox proportional hazard model is a procedure that is used in modeling data regarding time leading up to an event where censored cases exist. Whereas the Logistic regression model is mostly applicable in cases where the independent variables consist of numerical as well as nominal values while the resultant variable is binary (dichotomous). Arguments and findings of many researchers focused on the overview of Cox and Logistic regression models and their different applications in different areas. In this work, the analysis is done on secondary data whose source is SPSS exercise data on BREAST CANCER with a sample size of 1121 women where the main objective is to show the application difference between Cox regression model and logistic regression model based on factors that cause women to die due to breast cancer. Thus we did some analysis manually i.e. on lymph nodes status, and SPSS software helped to analyze the mentioned data. This study found out that there is an application difference between Cox and Logistic regression models which is Cox regression model is used if one wishes to analyze data which also include the follow-up time whereas Logistic regression model analyzes data without follow-up-time. Also, they have measurements of association which is different: hazard ratio and odds ratio for Cox and logistic regression models respectively. A similarity between the two models is that they are both applicable in the prediction of the upshot of a categorical variable i.e. a variable that can accommodate only a restricted number of categories. In conclusion, Cox regression model differs from logistic regression by assessing a rate instead of proportion. The two models can be applied in many other researches since they are suitable methods for analyzing data but the more recommended is the Cox, regression model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression%20model" title="logistic regression model">logistic regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=Cox%20regression%20model" title=" Cox regression model"> Cox regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20analysis" title=" survival analysis"> survival analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=hazard%20ratio" title=" hazard ratio"> hazard ratio</a> </p> <a href="https://publications.waset.org/abstracts/66111/application-difference-between-cox-and-logistic-regression-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66111.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">455</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3224</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/abstracts/search?q=Masahiro%20Ohmura">Masahiro Ohmura</a>, <a href="https://publications.waset.org/abstracts/search?q=Koh%20Kakusho"> Koh Kakusho</a>, <a href="https://publications.waset.org/abstracts/search?q=Takeshi%20Okadome"> Takeshi Okadome</a> </p> <p class="card-text"><strong>Abstract:</strong></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. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20prediction" title="stock market prediction">stock market prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20moods" title=" social moods"> social moods</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20model" title=" regression model"> regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=DJIA" title=" DJIA"> DJIA</a> </p> <a href="https://publications.waset.org/abstracts/8713/stock-market-prediction-by-regression-model-with-social-moods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8713.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">548</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3223</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/abstracts/search?q=Shiwei%20Deng">Shiwei Deng</a>, <a href="https://publications.waset.org/abstracts/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/abstracts/search?q=dependence%20analysis" title="dependence analysis">dependence analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=EFSM%20model" title=" EFSM model"> EFSM model</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20algorithm" title=" greedy algorithm"> greedy algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20test" title=" regression test"> regression test</a> </p> <a href="https://publications.waset.org/abstracts/31318/model-based-software-regression-test-suite-reduction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31318.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">427</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3222</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/abstracts/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=piecewise%20regression" title="piecewise regression">piecewise regression</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian" title=" bayesian"> bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20jump%20MCMC" title=" reversible jump MCMC"> reversible jump MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/46201/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/abstracts/46201.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">373</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3221</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/abstracts/search?q=Shih-Pin%20Chen">Shih-Pin Chen</a>, <a href="https://publications.waset.org/abstracts/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/abstracts/search?q=dissemblance%20index" title="dissemblance index">dissemblance index</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20linear%20regression" title=" fuzzy linear regression"> fuzzy linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=graded%20mean%20integration" title=" graded mean integration"> graded mean integration</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20programming" title=" mathematical programming"> mathematical programming</a> </p> <a href="https://publications.waset.org/abstracts/9968/a-fuzzy-linear-regression-model-based-on-dissemblance-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9968.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">439</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3220</span> The Theory behind Logistic Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jan%20Henrik%20Wosnitza">Jan Henrik Wosnitza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The logistic regression has developed into a standard approach for estimating conditional probabilities in a wide range of applications including credit risk prediction. The article at hand contributes to the current literature on logistic regression fourfold: First, it is demonstrated that the binary logistic regression automatically meets its model assumptions under very general conditions. This result explains, at least in part, the logistic regression's popularity. Second, the requirement of homoscedasticity in the context of binary logistic regression is theoretically substantiated. The variances among the groups of defaulted and non-defaulted obligors have to be the same across the level of the aggregated default indicators in order to achieve linear logits. Third, this article sheds some light on the question why nonlinear logits might be superior to linear logits in case of a small amount of data. Fourth, an innovative methodology for estimating correlations between obligor-specific log-odds is proposed. In order to crystallize the key ideas, this paper focuses on the example of credit risk prediction. However, the results presented in this paper can easily be transferred to any other field of application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation" title="correlation">correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20risk%20estimation" title=" credit risk estimation"> credit risk estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=default%20correlation" title=" default correlation"> default correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=homoscedasticity" title=" homoscedasticity"> homoscedasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20logistic%20regression" title=" nonlinear logistic regression"> nonlinear logistic regression</a> </p> <a href="https://publications.waset.org/abstracts/14339/the-theory-behind-logistic-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14339.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">426</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3219</span> Model Averaging for Poisson Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhou%20Jianhong">Zhou Jianhong </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Model averaging is a desirable approach to deal with model uncertainty, which, however, has rarely been explored for Poisson regression. In this paper, we propose a model averaging procedure based on an unbiased estimator of the expected Kullback-Leibler distance for the Poisson regression. Simulation study shows that the proposed model average estimator outperforms some other commonly used model selection and model average estimators in some situations. Our proposed methods are further applied to a real data example and the advantage of this method is demonstrated again. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20averaging" title="model averaging">model averaging</a>, <a href="https://publications.waset.org/abstracts/search?q=poission%20regression" title=" poission regression"> poission regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Kullback-Leibler%20distance" title=" Kullback-Leibler distance"> Kullback-Leibler distance</a>, <a href="https://publications.waset.org/abstracts/search?q=statistics" title=" statistics"> statistics</a> </p> <a href="https://publications.waset.org/abstracts/5501/model-averaging-for-poisson-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5501.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">520</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3218</span> Establishment of the Regression Uncertainty of the Critical Heat Flux Power Correlation for an Advanced Fuel Bundle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20Q.%20Yuan">L. Q. Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Yang"> J. Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Siddiqui"> A. Siddiqui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new regression uncertainty analysis methodology was applied to determine the uncertainties of the critical heat flux (CHF) power correlation for an advanced 43-element bundle design, which was developed by Canadian Nuclear Laboratories (CNL) to achieve improved economics, resource utilization and energy sustainability. The new methodology is considered more appropriate than the traditional methodology in the assessment of the experimental uncertainty associated with regressions. The methodology was first assessed using both the Monte Carlo Method (MCM) and the Taylor Series Method (TSM) for a simple linear regression model, and then extended successfully to a non-linear CHF power regression model (CHF power as a function of inlet temperature, outlet pressure and mass flow rate). The regression uncertainty assessed by MCM agrees well with that by TSM. An equation to evaluate the CHF power regression uncertainty was developed and expressed as a function of independent variables that determine the CHF power. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CHF%20experiment" title="CHF experiment">CHF experiment</a>, <a href="https://publications.waset.org/abstracts/search?q=CHF%20correlation" title=" CHF correlation"> CHF correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20uncertainty" title=" regression uncertainty"> regression uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20Method" title=" Monte Carlo Method"> Monte Carlo Method</a>, <a href="https://publications.waset.org/abstracts/search?q=Taylor%20Series%20Method" title=" Taylor Series Method"> Taylor Series Method</a> </p> <a href="https://publications.waset.org/abstracts/77556/establishment-of-the-regression-uncertainty-of-the-critical-heat-flux-power-correlation-for-an-advanced-fuel-bundle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77556.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">416</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3217</span> Non-Parametric Regression over Its Parametric Couterparts with Large Sample Size</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jude%20Opara">Jude Opara</a>, <a href="https://publications.waset.org/abstracts/search?q=Esemokumo%20Perewarebo%20Akpos"> Esemokumo Perewarebo Akpos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is on non-parametric linear regression over its parametric counterparts with large sample size. Data set on anthropometric measurement of primary school pupils was taken for the analysis. The study used 50 randomly selected pupils for the study. The set of data was subjected to normality test, and it was discovered that the residuals are not normally distributed (i.e. they do not follow a Gaussian distribution) for the commonly used least squares regression method for fitting an equation into a set of (x,y)-data points using the Anderson-Darling technique. The algorithms for the nonparametric Theil’s regression are stated in this paper as well as its parametric OLS counterpart. The use of a programming language software known as “R Development” was used in this paper. From the analysis, the result showed that there exists a significant relationship between the response and the explanatory variable for both the parametric and non-parametric regression. To know the efficiency of one method over the other, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) are used, and it is discovered that the nonparametric regression performs better than its parametric regression counterparts due to their lower values in both the AIC and BIC. The study however recommends that future researchers should study a similar work by examining the presence of outliers in the data set, and probably expunge it if detected and re-analyze to compare results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Theil%E2%80%99s%20regression" title="Theil’s regression">Theil’s regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20information%20criterion" title=" Bayesian information criterion"> Bayesian information criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=Akaike%20information%20criterion" title=" Akaike information criterion"> Akaike information criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=OLS" title=" OLS"> OLS</a> </p> <a href="https://publications.waset.org/abstracts/58536/non-parametric-regression-over-its-parametric-couterparts-with-large-sample-size" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58536.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">305</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3216</span> Use of Multistage Transition Regression Models for Credit Card Income Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Denys%20Osipenko">Denys Osipenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Crook"> Jonathan Crook</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multinomial%20regression" title="multinomial regression">multinomial regression</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20logistic%20regression" title=" conditional logistic regression"> conditional logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20account%20state" title=" credit account state"> credit account state</a>, <a href="https://publications.waset.org/abstracts/search?q=transition%20probability" title=" transition probability"> transition probability</a> </p> <a href="https://publications.waset.org/abstracts/19488/use-of-multistage-transition-regression-models-for-credit-card-income-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19488.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">487</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3215</span> Semiparametric Regression Of Truncated Spline Biresponse On Farmer Loyalty And Attachment Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adji%20Achmad%20Rinaldo%20Fernandes">Adji Achmad Rinaldo Fernandes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Regression analysis is a statistical method that is able to describe and predict causal relationships between individuals. Not all relationships have a known curve shape; often, there are relationship patterns that cannot be known in the shape of the curve; besides that, a cause can have an impact on more than one effect, so that between effects can also have a close relationship in it. Regression analysis that can be done to find out the relationship can be brought closer to the semiparametric regression of truncated spline biresponse. The purpose of this study is to examine the function estimator and determine the best model of truncated spline biresponse semiparametric regression. The results of the secondary data study showed that the best model with the highest order of quadratic and a maximum of two knots with a Goodness of fit value in the form of Adjusted R2 of 88.5%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biresponse" title="biresponse">biresponse</a>, <a href="https://publications.waset.org/abstracts/search?q=farmer%20attachment" title=" farmer attachment"> farmer attachment</a>, <a href="https://publications.waset.org/abstracts/search?q=farmer%20loyalty" title=" farmer loyalty"> farmer loyalty</a>, <a href="https://publications.waset.org/abstracts/search?q=truncated%20spline" title=" truncated spline"> truncated spline</a> </p> <a href="https://publications.waset.org/abstracts/186759/semiparametric-regression-of-truncated-spline-biresponse-on-farmer-loyalty-and-attachment-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186759.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">36</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3214</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/abstracts/search?q=Ksenija%20Dumi%C4%8Di%C4%87">Ksenija Dumičić</a>, <a href="https://publications.waset.org/abstracts/search?q=Anita%20%C4%8Ceh%20%C4%8Casni"> Anita Čeh Časni</a>, <a href="https://publications.waset.org/abstracts/search?q=Irena%20Pali%C4%87"> Irena Palić</a> </p> <p class="card-text"><strong>Abstract:</strong></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 analysed EU countries is positively correlated with statistically significant variable Gross Domestic Product per capita (GDPpc). Also, analysed 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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=European%20union" title="European union">European union</a>, <a href="https://publications.waset.org/abstracts/search?q=Internet%20purchases" title=" Internet purchases"> Internet purchases</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20linear%20regression%20model" title=" multiple linear regression model"> multiple linear regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier" title=" outlier"> outlier</a> </p> <a href="https://publications.waset.org/abstracts/2650/internet-purchases-in-european-union-countries-multiple-linear-regression-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2650.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">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3213</span> Copula-Based Estimation of Direct and Indirect Effects in Path Analysis Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alam%20Ali">Alam Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashok%20Kumar%20Pathak"> Ashok Kumar Pathak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Path analysis is a statistical technique used to evaluate the direct and indirect effects of variables in path models. One or more structural regression equations are used to estimate a series of parameters in path models to find the better fit of data. However, sometimes the assumptions of classical regression models, such as ordinary least squares (OLS), are violated by the nature of the data, resulting in insignificant direct and indirect effects of exogenous variables. This article aims to explore the effectiveness of a copula-based regression approach as an alternative to classical regression, specifically when variables are linked through an elliptical copula. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=path%20analysis" title="path analysis">path analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=copula-based%20regression%20models" title=" copula-based regression models"> copula-based regression models</a>, <a href="https://publications.waset.org/abstracts/search?q=direct%20and%20indirect%20effects" title=" direct and indirect effects"> direct and indirect effects</a>, <a href="https://publications.waset.org/abstracts/search?q=k-fold%20cross%20validation%20technique" title=" k-fold cross validation technique"> k-fold cross validation technique</a> </p> <a href="https://publications.waset.org/abstracts/186900/copula-based-estimation-of-direct-and-indirect-effects-in-path-analysis-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186900.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">41</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3212</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/abstracts/search?q=Galal%20Elkobrosy">Galal Elkobrosy</a>, <a href="https://publications.waset.org/abstracts/search?q=Amr%20M.%20Abdelrazek"> Amr M. Abdelrazek</a>, <a href="https://publications.waset.org/abstracts/search?q=Bassuny%20M.%20Elsouhily"> Bassuny M. Elsouhily</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20E.%20Khidr"> Mohamed E. Khidr</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=design%20of%20experiments" title="design of experiments">design of experiments</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20analysis" title=" regression analysis"> regression analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=SI%20engine" title=" SI engine"> SI engine</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20modeling" title=" statistical modeling"> statistical modeling</a> </p> <a href="https://publications.waset.org/abstracts/90228/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/abstracts/90228.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">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3211</span> An Epsilon Hierarchical Fuzzy Twin Support Vector Regression </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arindam%20Chaudhuri">Arindam Chaudhuri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research presents epsilon- hierarchical fuzzy twin support vector regression (epsilon-HFTSVR) based on epsilon-fuzzy twin support vector regression (epsilon-FTSVR) and epsilon-twin support vector regression (epsilon-TSVR). Epsilon-FTSVR is achieved by incorporating trapezoidal fuzzy numbers to epsilon-TSVR which takes care of uncertainty existing in forecasting problems. Epsilon-FTSVR determines a pair of epsilon-insensitive proximal functions by solving two related quadratic programming problems. The structural risk minimization principle is implemented by introducing regularization term in primal problems of epsilon-FTSVR. This yields dual stable positive definite problems which improves regression performance. Epsilon-FTSVR is then reformulated as epsilon-HFTSVR consisting of a set of hierarchical layers each containing epsilon-FTSVR. Experimental results on both synthetic and real datasets reveal that epsilon-HFTSVR has remarkable generalization performance with minimum training time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=regression" title="regression">regression</a>, <a href="https://publications.waset.org/abstracts/search?q=epsilon-TSVR" title=" epsilon-TSVR"> epsilon-TSVR</a>, <a href="https://publications.waset.org/abstracts/search?q=epsilon-FTSVR" title=" epsilon-FTSVR"> epsilon-FTSVR</a>, <a href="https://publications.waset.org/abstracts/search?q=epsilon-HFTSVR" title=" epsilon-HFTSVR"> epsilon-HFTSVR</a> </p> <a href="https://publications.waset.org/abstracts/20236/an-epsilon-hierarchical-fuzzy-twin-support-vector-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20236.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">375</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3210</span> The Effect of Penalizing Wrong Answers in the Computerized Modified Multiple Choice Testing System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Min%20Hae%20Song">Min Hae Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Jooyong%20Park"> Jooyong Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Even though assessment using information and communication technology will most likely lead the future of educational assessment, there is little research on this topic. Computerized assessment will not only cut costs but also measure students' performance in ways not possible before. In this context, this study introduces a tool which can overcome the problems of multiple choice tests. Multiple-choice tests (MC) are efficient in automatic grading, however structural problems of multiple-choice tests allow students to find the correct answer from options even though they do not know the answer. A computerized modified multiple-choice testing system (CMMT) was developed using the interactivity of computers, that presents questions first, and options later for a short time when the student requests for them. This study was conducted to find out whether penalizing for wrong answers in CMMT could lower random guessing. In this study, we checked whether students knew the answers by having them respond to the short-answer tests before choosing the given options in CMMT or MC format. Ninety-four students were tested with the directions that they will be penalized for wrong answers, but not for no response. There were 4 experimental conditions: two conditions of high or low percentage of penalizing, each in traditional multiple-choice or CMMT format. In the low penalty condition, the penalty rate was the probability of getting the correct answer by random guessing. In the high penalty condition, students were penalized at twice the percentage of the low penalty condition. The results showed that the number of no response was significantly higher for the CMMT format and the number of random guesses was significantly lower for the CMMT format. There were no significant between the two penalty conditions. This result may be due to the fact that the actual score difference between the two conditions was too small. In the discussion, the possibility of applying CMMT format tests while penalizing wrong answers in actual testing settings was addressed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computerized%20modified%20multiple%20choice%20test%20format" title="computerized modified multiple choice test format">computerized modified multiple choice test format</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple-choice%20test%20format" title=" multiple-choice test format"> multiple-choice test format</a>, <a href="https://publications.waset.org/abstracts/search?q=penalizing" title=" penalizing"> penalizing</a>, <a href="https://publications.waset.org/abstracts/search?q=test%20format" title=" test format"> test format</a> </p> <a href="https://publications.waset.org/abstracts/92283/the-effect-of-penalizing-wrong-answers-in-the-computerized-modified-multiple-choice-testing-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92283.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">167</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=penalized%20regression&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=penalized%20regression&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=penalized%20regression&page=4">4</a></li> <li class="page-item"><a class="page-link" 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