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estimator</title> <meta name="description" content="Search results for: robust estimator"> <meta name="keywords" content="robust estimator"> <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="robust estimator"> <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> 1623</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: robust estimator</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1623</span> Alternative Robust Estimators for the Shape Parameters of the Burr XII Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Zehra%20Do%C4%9Fru">Fatma Zehra Doğru</a>, <a href="https://publications.waset.org/abstracts/search?q=Olcay%20Arslan"> Olcay Arslan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose alternative robust estimators for the shape parameters of the Burr XII distribution. We provide a small simulation study and a real data example to illustrate the performance of the proposed estimators over the ML and the LS estimators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=burr%20xii%20distribution" title="burr xii distribution">burr xii distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20estimator" title=" robust estimator"> robust estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=m-estimator" title=" m-estimator"> m-estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20squares" title=" least squares"> least squares</a> </p> <a href="https://publications.waset.org/abstracts/30038/alternative-robust-estimators-for-the-shape-parameters-of-the-burr-xii-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30038.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">428</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">1622</span> Unit Root Tests Based On the Robust Estimator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wararit%20Panichkitkosolkul">Wararit Panichkitkosolkul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p class="Abstract" style="text-indent:10.2pt">The unit root tests based on the robust estimator for the first-order autoregressive process are proposed and compared with the unit root tests based on the ordinary least squares (OLS) estimator. The percentiles of the null distributions of the unit root test are also reported. The empirical probabilities of Type I error and powers of the unit root tests are estimated via Monte Carlo simulation. Simulation results show that all unit root tests can control the probability of Type I error for all situations. The empirical power of the unit root tests based on the robust estimator are higher than the unit root tests based on the OLS estimator.<o:p></o:p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autoregressive" title="autoregressive">autoregressive</a>, <a href="https://publications.waset.org/abstracts/search?q=ordinary%20least%20squares" title=" ordinary least squares"> ordinary least squares</a>, <a href="https://publications.waset.org/abstracts/search?q=type%20i%20error" title=" type i error"> type i error</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20of%20the%20test" title=" power of the test"> power of the test</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a> </p> <a href="https://publications.waset.org/abstracts/3693/unit-root-tests-based-on-the-robust-estimator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3693.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">288</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">1621</span> Robust Shrinkage Principal Component Parameter Estimator for Combating Multicollinearity and Outliers’ Problems in a Poisson Regression Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arum%20Kingsley%20Chinedu">Arum Kingsley Chinedu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ugwuowo%20Fidelis%20Ifeanyi"> Ugwuowo Fidelis Ifeanyi</a>, <a href="https://publications.waset.org/abstracts/search?q=Oranye%20Henrietta%20Ebele"> Oranye Henrietta Ebele</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Poisson regression model (PRM) is a nonlinear model that belongs to the exponential family of distribution. PRM is suitable for studying count variables using appropriate covariates and sometimes experiences the problem of multicollinearity in the explanatory variables and outliers on the response variable. This study aims to address the problem of multicollinearity and outliers jointly in a Poisson regression model. We developed an estimator called the robust modified jackknife PCKL parameter estimator by combining the principal component estimator, modified jackknife KL and transformed M-estimator estimator to address both problems in a PRM. The superiority conditions for this estimator were established, and the properties of the estimator were also derived. The estimator inherits the characteristics of the combined estimators, thereby making it efficient in addressing both problems. And will also be of immediate interest to the research community and advance this study in terms of novelty compared to other studies undertaken in this area. The performance of the estimator (robust modified jackknife PCKL) with other existing estimators was compared using mean squared error (MSE) as a performance evaluation criterion through a Monte Carlo simulation study and the use of real-life data. The results of the analytical study show that the estimator outperformed other existing estimators compared with by having the smallest MSE across all sample sizes, different levels of correlation, percentages of outliers and different numbers of explanatory variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=jackknife%20modified%20KL" title="jackknife modified KL">jackknife modified KL</a>, <a href="https://publications.waset.org/abstracts/search?q=outliers" title=" outliers"> outliers</a>, <a href="https://publications.waset.org/abstracts/search?q=multicollinearity" title=" multicollinearity"> multicollinearity</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component" title=" principal component"> principal component</a>, <a href="https://publications.waset.org/abstracts/search?q=transformed%20M-estimator." title=" transformed M-estimator."> transformed M-estimator.</a> </p> <a href="https://publications.waset.org/abstracts/183536/robust-shrinkage-principal-component-parameter-estimator-for-combating-multicollinearity-and-outliers-problems-in-a-poisson-regression-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183536.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">66</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">1620</span> Model Averaging in a Multiplicative Heteroscedastic Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alan%20Wan">Alan Wan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, the body of literature on frequentist model averaging in statistics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this paper, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimisation of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favourable properties compared to some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function misspecification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two real data sets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heteroscedasticity-robust" title="heteroscedasticity-robust">heteroscedasticity-robust</a>, <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=multiplicative%20heteroscedasticity" title=" multiplicative heteroscedasticity"> multiplicative heteroscedasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=plug-in" title=" plug-in"> plug-in</a>, <a href="https://publications.waset.org/abstracts/search?q=squared%20prediction%20risk" title=" squared prediction risk"> squared prediction risk</a> </p> <a href="https://publications.waset.org/abstracts/68733/model-averaging-in-a-multiplicative-heteroscedastic-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68733.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">384</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">1619</span> Practical Techniques of Improving State Estimator Solution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kiamran%20Radjabli">Kiamran Radjabli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> State Estimator became an intrinsic part of Energy Management Systems (EMS). The SCADA measurements received from the field are processed by the State Estimator in order to accurately determine the actual operating state of the power systems and provide that information to other real-time network applications. All EMS vendors offer a State Estimator functionality in their baseline products. However, setting up and ensuring that State Estimator consistently produces a reliable solution often consumes a substantial engineering effort. This paper provides generic recommendations and describes a simple practical approach to efficient tuning of State Estimator, based on the working experience with major EMS software platforms and consulting projects in many electrical utilities of the USA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convergence" title="convergence">convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimator" title=" state estimator"> state estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=troubleshooting" title=" troubleshooting"> troubleshooting</a>, <a href="https://publications.waset.org/abstracts/search?q=tuning" title=" tuning"> tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20systems" title=" power systems"> power systems</a> </p> <a href="https://publications.waset.org/abstracts/124338/practical-techniques-of-improving-state-estimator-solution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124338.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">156</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">1618</span> Robust Variable Selection Based on Schwarz Information Criterion for Linear Regression Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shokrya%20Saleh%20A.%20Alshqaq">Shokrya Saleh A. Alshqaq</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Ali%20H.%20Ahmadini"> Abdullah Ali H. Ahmadini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Schwarz information criterion (SIC) is a popular tool for selecting the best variables in regression datasets. However, SIC is defined using an unbounded estimator, namely, the least-squares (LS), which is highly sensitive to outlying observations, especially bad leverage points. A method for robust variable selection based on SIC for linear regression models is thus needed. This study investigates the robustness properties of SIC by deriving its influence function and proposes a robust SIC based on the MM-estimation scale. The aim of this study is to produce a criterion that can effectively select accurate models in the presence of vertical outliers and high leverage points. The advantages of the proposed robust SIC is demonstrated through a simulation study and an analysis of a real dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=influence%20function" title="influence function">influence function</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20variable%20selection" title=" robust variable selection"> robust variable selection</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20regression" title=" robust regression"> robust regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Schwarz%20information%20criterion" title=" Schwarz information criterion"> Schwarz information criterion</a> </p> <a href="https://publications.waset.org/abstracts/131338/robust-variable-selection-based-on-schwarz-information-criterion-for-linear-regression-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131338.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">139</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">1617</span> Comparison between Some of Robust Regression Methods with OLS Method with Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sizar%20Abed%20Mohammed">Sizar Abed Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Zahraa%20Ghazi%20Sadeeq"> Zahraa Ghazi Sadeeq </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of the classic method, least squares (OLS) to estimate the linear regression parameters, when they are available assumptions, and capabilities that have good characteristics, such as impartiality, minimum variance, consistency, and so on. The development of alternative statistical techniques to estimate the parameters, when the data are contaminated with outliers. These are powerful methods (or resistance). In this paper, three of robust methods are studied, which are: Maximum likelihood type estimate M-estimator, Modified Maximum likelihood type estimate MM-estimator and Least Trimmed Squares LTS-estimator, and their results are compared with OLS method. These methods applied to real data taken from Duhok company for manufacturing furniture, the obtained results compared by using the criteria: Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Sum of Absolute Error (MSAE). Important conclusions that this study came up with are: a number of typical values detected by using four methods in the furniture line and very close to the data. This refers to the fact that close to the normal distribution of standard errors, but typical values in the doors line data, using OLS less than that detected by the powerful ways. This means that the standard errors of the distribution are far from normal departure. Another important conclusion is that the estimated values of the parameters by using the lifeline is very far from the estimated values using powerful methods for line doors, gave LTS- destined better results using standard MSE, and gave the M- estimator better results using standard MAPE. Moreover, we noticed that using standard MSAE, and MM- estimator is better. The programs S-plus (version 8.0, professional 2007), Minitab (version 13.2) and SPSS (version 17) are used to analyze the data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Robest" title="Robest">Robest</a>, <a href="https://publications.waset.org/abstracts/search?q=LTS" title=" LTS"> LTS</a>, <a href="https://publications.waset.org/abstracts/search?q=M%20estimate" title=" M estimate"> M estimate</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a> </p> <a href="https://publications.waset.org/abstracts/53677/comparison-between-some-of-robust-regression-methods-with-ols-method-with-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53677.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">232</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">1616</span> Ratio Type Estimators for the Estimation of Population Coefficient of Variation under Two-Stage Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Jabbar">Muhammad Jabbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose two ratio and ratio type exponential estimator for the estimation of population coefficient of variation using the auxiliary information under two-stage sampling. The properties of these estimators are derived up to first order of approximation. The efficiency conditions under which suggested estimator are more efficient, are obtained. Numerical and simulated studies are conducted to support the superiority of the estimators. Theoretically and numerically, we have found that our proposed estimator is always more efficient as compared to its competitor estimator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=two-stage%20sampling" title="two-stage sampling">two-stage sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=coefficient%20of%20variation" title=" coefficient of variation"> coefficient of variation</a>, <a href="https://publications.waset.org/abstracts/search?q=ratio%20type%20exponential%20estimator" title=" ratio type exponential estimator"> ratio type exponential estimator</a> </p> <a href="https://publications.waset.org/abstracts/21936/ratio-type-estimators-for-the-estimation-of-population-coefficient-of-variation-under-two-stage-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21936.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">528</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">1615</span> Comparing Xbar Charts: Conventional versus Reweighted Robust Estimation Methods for Univariate Data Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ece%20Cigdem%20Mutlu">Ece Cigdem Mutlu</a>, <a href="https://publications.waset.org/abstracts/search?q=Burak%20Alakent"> Burak Alakent</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Maintaining the quality of manufactured products at a desired level depends on the stability of process dispersion and location parameters and detection of perturbations in these parameters as promptly as possible. Shewhart control chart is the most widely used technique in statistical process monitoring to monitor the quality of products and control process mean and variability. In the application of Xbar control charts, sample standard deviation and sample mean are known to be the most efficient conventional estimators in determining process dispersion and location parameters, respectively, based on the assumption of independent and normally distributed datasets. On the other hand, there is no guarantee that the real-world data would be normally distributed. In the cases of estimated process parameters from Phase I data clouded with outliers, efficiency of traditional estimators is significantly reduced, and performance of Xbar charts are undesirably low, e.g. occasional outliers in the rational subgroups in Phase I data set may considerably affect the sample mean and standard deviation, resulting a serious delay in detection of inferior products in Phase II. For more efficient application of control charts, it is required to use robust estimators against contaminations, which may exist in Phase I. In the current study, we present a simple approach to construct robust Xbar control charts using average distance to the median, Qn-estimator of scale, M-estimator of scale with logistic psi-function in the estimation of process dispersion parameter, and Harrell-Davis qth quantile estimator, Hodge-Lehmann estimator and M-estimator of location with Huber psi-function and logistic psi-function in the estimation of process location parameter. Phase I efficiency of proposed estimators and Phase II performance of Xbar charts constructed from these estimators are compared with the conventional mean and standard deviation statistics both under normality and against diffuse-localized and symmetric-asymmetric contaminations using 50,000 Monte Carlo simulations on MATLAB. Consequently, it is found that robust estimators yield parameter estimates with higher efficiency against all types of contaminations, and Xbar charts constructed using robust estimators have higher power in detecting disturbances, compared to conventional methods. Additionally, utilizing individuals charts to screen outlier subgroups and employing different combination of dispersion and location estimators on subgroups and individual observations are found to improve the performance of Xbar charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=M-estimators" title=" M-estimators"> M-estimators</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20estimators" title=" robust estimators"> robust estimators</a> </p> <a href="https://publications.waset.org/abstracts/79020/comparing-xbar-charts-conventional-versus-reweighted-robust-estimation-methods-for-univariate-data-sets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79020.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">190</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">1614</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">474</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">1613</span> Estimation of Stress-Strength Parameter for Burr Type XII Distribution Based on Progressive Type-II Censoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20M.%20Abd-Elfattah">A. M. Abd-Elfattah</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Abu-Moussa"> M. H. Abu-Moussa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the estimation of stress-strength parameter R = P(Y < X) is considered when X; Y the strength and stress respectively are two independent random variables of Burr Type XII distribution. The samples taken for X and Y are progressively censoring of type II. The maximum likelihood estimator (MLE) of R is obtained when the common parameter is unknown. But when the common parameter is known the MLE, uniformly minimum variance unbiased estimator (UMVUE) and the Bayes estimator of R = P(Y < X) are obtained. The exact condence interval of R based on MLE is obtained. The performance of the proposed estimators is compared using the computer simulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Burr%20Type%20XII%20distribution" title="Burr Type XII distribution">Burr Type XII distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=progressive%20type-II%20censoring" title=" progressive type-II censoring"> progressive type-II censoring</a>, <a href="https://publications.waset.org/abstracts/search?q=stress-strength%20model" title=" stress-strength model"> stress-strength model</a>, <a href="https://publications.waset.org/abstracts/search?q=unbiased%20estimator" title=" unbiased estimator"> unbiased estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum-likelihood%20estimator" title=" maximum-likelihood estimator"> maximum-likelihood estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=uniformly%20minimum%20variance%20unbiased%20estimator" title=" uniformly minimum variance unbiased estimator"> uniformly minimum variance unbiased estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=confidence%20intervals" title=" confidence intervals"> confidence intervals</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayes%20estimator" title=" Bayes estimator"> Bayes estimator</a> </p> <a href="https://publications.waset.org/abstracts/15905/estimation-of-stress-strength-parameter-for-burr-type-xii-distribution-based-on-progressive-type-ii-censoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15905.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">456</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">1612</span> On the Performance of Improvised Generalized M-Estimator in the Presence of High Leverage Collinearity Enhancing Observations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Habshah%20Midi">Habshah Midi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20A.%20Mohammed"> Mohammed A. Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Sohel%20Rana"> Sohel Rana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multicollinearity occurs when two or more independent variables in a multiple linear regression model are highly correlated. The ridge regression is the commonly used method to rectify this problem. However, the ridge regression cannot handle the problem of multicollinearity which is caused by high leverage collinearity enhancing observation (HLCEO). Since high leverage points (HLPs) are responsible for inducing multicollinearity, the effect of HLPs needs to be reduced by using Generalized M estimator. The existing GM6 estimator is based on the Minimum Volume Ellipsoid (MVE) which tends to swamp some low leverage points. Hence an improvised GM (MGM) estimator is presented to improve the precision of the GM6 estimator. Numerical example and simulation study are presented to show how HLPs can cause multicollinearity. The numerical results show that our MGM estimator is the most efficient method compared to some existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=identification" title="identification">identification</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20leverage%20points" title=" high leverage points"> high leverage points</a>, <a href="https://publications.waset.org/abstracts/search?q=multicollinearity" title=" multicollinearity"> multicollinearity</a>, <a href="https://publications.waset.org/abstracts/search?q=GM-estimator" title=" GM-estimator"> GM-estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=DRGP" title=" DRGP"> DRGP</a>, <a href="https://publications.waset.org/abstracts/search?q=DFFITS" title=" DFFITS"> DFFITS</a> </p> <a href="https://publications.waset.org/abstracts/55345/on-the-performance-of-improvised-generalized-m-estimator-in-the-presence-of-high-leverage-collinearity-enhancing-observations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55345.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">262</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">1611</span> Variogram Fitting Based on the Wilcoxon Norm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hazem%20Al-Mofleh">Hazem Al-Mofleh</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Daniels"> John Daniels</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20McKean"> Joseph McKean</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Within geostatistics research, effective estimation of the variogram points has been examined, particularly in developing robust alternatives. The parametric fit of these variogram points which eventually defines the kriging weights, however, has not received the same attention from a robust perspective. This paper proposes the use of the non-linear Wilcoxon norm over weighted non-linear least squares as a robust variogram fitting alternative. First, we introduce the concept of variogram estimation and fitting. Then, as an alternative to non-linear weighted least squares, we discuss the non-linear Wilcoxon estimator. Next, the robustness properties of the non-linear Wilcoxon are demonstrated using a contaminated spatial data set. Finally, under simulated conditions, increasing levels of contaminated spatial processes have their variograms points estimated and fit. In the fitting of these variogram points, both non-linear Weighted Least Squares and non-linear Wilcoxon fits are examined for efficiency. At all levels of contamination (including 0%), using a robust estimation and robust fitting procedure, the non-weighted Wilcoxon outperforms weighted Least Squares. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-linear%20wilcoxon" title="non-linear wilcoxon">non-linear wilcoxon</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20estimation" title=" robust estimation"> robust estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=variogram%20estimation" title=" variogram estimation"> variogram estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=wilcoxon%20norm" title=" wilcoxon norm"> wilcoxon norm</a> </p> <a href="https://publications.waset.org/abstracts/50377/variogram-fitting-based-on-the-wilcoxon-norm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50377.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">458</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">1610</span> The Linear Combination of Kernels in the Estimation of the Cumulative Distribution Functions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdel-Razzaq%20Mugdadi">Abdel-Razzaq Mugdadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruqayyah%20Sani"> Ruqayyah Sani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Kernel Distribution Function Estimator (KDFE) method is the most popular method for nonparametric estimation of the cumulative distribution function. The kernel and the bandwidth are the most important components of this estimator. In this investigation, we replace the kernel in the KDFE with a linear combination of kernels to obtain a new estimator based on the linear combination of kernels, the mean integrated squared error (MISE), asymptotic mean integrated squared error (AMISE) and the asymptotically optimal bandwidth for the new estimator are derived. We propose a new data-based method to select the bandwidth for the new estimator. The new technique is based on the Plug-in technique in density estimation. We evaluate the new estimator and the new technique using simulations and real-life data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=estimation" title="estimation">estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=bandwidth" title=" bandwidth"> bandwidth</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20square%20error" title=" mean square error"> mean square error</a>, <a href="https://publications.waset.org/abstracts/search?q=cumulative%20distribution%20function" title=" cumulative distribution function"> cumulative distribution function</a> </p> <a href="https://publications.waset.org/abstracts/28571/the-linear-combination-of-kernels-in-the-estimation-of-the-cumulative-distribution-functions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28571.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">581</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">1609</span> Introduction of Robust Multivariate Process Capability Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Behrooz%20Khalilloo">Behrooz Khalilloo</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Shahriari"> Hamid Shahriari</a>, <a href="https://publications.waset.org/abstracts/search?q=Emad%20Roghanian"> Emad Roghanian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process capability indices (PCIs) are important concepts of statistical quality control and measure the capability of processes and how much processes are meeting certain specifications. An important issue in statistical quality control is parameter estimation. Under the assumption of multivariate normality, the distribution parameters, mean vector and variance-covariance matrix must be estimated, when they are unknown. Classic estimation methods like method of moment estimation (MME) or maximum likelihood estimation (MLE) makes good estimation of the population parameters when data are not contaminated. But when outliers exist in the data, MME and MLE make weak estimators of the population parameters. So we need some estimators which have good estimation in the presence of outliers. In this work robust M-estimators for estimating these parameters are used and based on robust parameter estimators, robust process capability indices are introduced. The performances of these robust estimators in the presence of outliers and their effects on process capability indices are evaluated by real and simulated multivariate data. The results indicate that the proposed robust capability indices perform much better than the existing process capability indices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multivariate%20process%20capability%20indices" title="multivariate process capability indices">multivariate process capability indices</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20M-estimator" title=" robust M-estimator"> robust M-estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier" title=" outlier"> outlier</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20quality%20control" title=" multivariate quality control"> multivariate quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20quality%20control" title=" statistical quality control"> statistical quality control</a> </p> <a href="https://publications.waset.org/abstracts/81586/introduction-of-robust-multivariate-process-capability-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81586.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">283</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">1608</span> Estimation of Rare and Clustered Population Mean Using Two Auxiliary Variables in Adaptive Cluster Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Nouman%20Qureshi">Muhammad Nouman Qureshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Hanif"> Muhammad Hanif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Adaptive cluster sampling (ACS) is specifically developed for the estimation of highly clumped populations and applied to a wide range of situations like animals of rare and endangered species, uneven minerals, HIV patients and drug users. In this paper, we proposed a generalized semi-exponential estimator with two auxiliary variables under the framework of ACS design. The expressions of approximate bias and mean square error (MSE) of the proposed estimator are derived. Theoretical comparisons of the proposed estimator have been made with existing estimators. A numerical study is conducted on real and artificial populations to demonstrate and compare the efficiencies of the proposed estimator. The results indicate that the proposed generalized semi-exponential estimator performed considerably better than all the adaptive and non-adaptive estimators considered in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auxiliary%20information" title="auxiliary information">auxiliary information</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20cluster%20sampling" title=" adaptive cluster sampling"> adaptive cluster sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=clustered%20populations" title=" clustered populations"> clustered populations</a>, <a href="https://publications.waset.org/abstracts/search?q=Hansen-Hurwitz%20estimation" title=" Hansen-Hurwitz estimation"> Hansen-Hurwitz estimation</a> </p> <a href="https://publications.waset.org/abstracts/98047/estimation-of-rare-and-clustered-population-mean-using-two-auxiliary-variables-in-adaptive-cluster-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98047.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">238</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">1607</span> On Estimating the Headcount Index by Using the Logistic Regression Estimator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Encarnaci%C3%B3n%20%C3%81lvarez">Encarnación Álvarez</a>, <a href="https://publications.waset.org/abstracts/search?q=Rosa%20M.%20Garc%C3%ADa-Fern%C3%A1ndez"> Rosa M. García-Fernández</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20F.%20Mu%C3%B1oz"> Juan F. Muñoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20J.%20Blanco-Encomienda"> Francisco J. Blanco-Encomienda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The problem of estimating a proportion has important applications in the field of economics, and in general, in many areas such as social sciences. A common application in economics is the estimation of the headcount index. In this paper, we define the general headcount index as a proportion. Furthermore, we introduce a new quantitative method for estimating the headcount index. In particular, we suggest to use the logistic regression estimator for the problem of estimating the headcount index. Assuming a real data set, results derived from Monte Carlo simulation studies indicate that the logistic regression estimator can be more accurate than the traditional estimator of the headcount index. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=poverty%20line" title="poverty line">poverty line</a>, <a href="https://publications.waset.org/abstracts/search?q=poor" title=" poor"> poor</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20of%20poverty" title=" risk of poverty"> risk of poverty</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulations" title=" Monte Carlo simulations"> Monte Carlo simulations</a>, <a href="https://publications.waset.org/abstracts/search?q=sample" title=" sample"> sample</a> </p> <a href="https://publications.waset.org/abstracts/7876/on-estimating-the-headcount-index-by-using-the-logistic-regression-estimator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7876.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">422</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">1606</span> Numerical Implementation and Testing of Fractioning Estimator Method for the Box-Counting Dimension of Fractal Objects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abraham%20Ter%C3%A1n%20Salcedo">Abraham Terán Salcedo</a>, <a href="https://publications.waset.org/abstracts/search?q=Didier%20Samayoa%20Ochoa"> Didier Samayoa Ochoa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a numerical implementation of a method for estimating the box-counting dimension of self-avoiding curves on a planar space, fractal objects captured on digital images; this method is named fractioning estimator. Classical methods of digital image processing, such as noise filtering, contrast manipulation, and thresholding, among others, are used in order to obtain binary images that are suitable for performing the necessary computations of the fractioning estimator. A user interface is developed for performing the image processing operations and testing the fractioning estimator on different captured images of real-life fractal objects. To analyze the results, the estimations obtained through the fractioning estimator are compared to the results obtained through other methods that are already implemented on different available software for computing and estimating the box-counting dimension. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=box-counting" title="box-counting">box-counting</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20image%20processing" title=" digital image processing"> digital image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20method" title=" numerical method"> numerical method</a> </p> <a href="https://publications.waset.org/abstracts/160901/numerical-implementation-and-testing-of-fractioning-estimator-method-for-the-box-counting-dimension-of-fractal-objects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160901.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">83</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">1605</span> Discrete Estimation of Spectral Density for Alpha Stable Signals Observed with an Additive Error</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Sabre">R. Sabre</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Horrigue"> W. Horrigue</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20C.%20Simon"> J. C. Simon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is interested in two difficulties encountered in practice when observing a continuous time process. The first is that we cannot observe a process over a time interval; we only take discrete observations. The second is the process frequently observed with a constant additive error. It is important to give an estimator of the spectral density of such a process taking into account the additive observation error and the choice of the discrete observation times. In this work, we propose an estimator based on the spectral smoothing of the periodogram by the polynomial Jackson kernel reducing the additive error. In order to solve the aliasing phenomenon, this estimator is constructed from observations taken at well-chosen times so as to reduce the estimator to the field where the spectral density is not zero. We show that the proposed estimator is asymptotically unbiased and consistent. Thus we obtain an estimate solving the two difficulties concerning the choice of the instants of observations of a continuous time process and the observations affected by a constant error. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spectral%20density" title="spectral density">spectral density</a>, <a href="https://publications.waset.org/abstracts/search?q=stable%20processes" title=" stable processes"> stable processes</a>, <a href="https://publications.waset.org/abstracts/search?q=aliasing" title=" aliasing"> aliasing</a>, <a href="https://publications.waset.org/abstracts/search?q=periodogram" title=" periodogram"> periodogram</a> </p> <a href="https://publications.waset.org/abstracts/118023/discrete-estimation-of-spectral-density-for-alpha-stable-signals-observed-with-an-additive-error" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118023.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">138</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">1604</span> Survival and Hazard Maximum Likelihood Estimator with Covariate Based on Right Censored Data of Weibull Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Al%20Omari%20Mohammed%20Ahmed">Al Omari Mohammed Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on Maximum Likelihood Estimator with Covariate. Covariates are incorporated into the Weibull model. Under this regression model with regards to maximum likelihood estimator, the parameters of the covariate, shape parameter, survival function and hazard rate of the Weibull regression distribution with right censored data are estimated. The mean square error (MSE) and absolute bias are used to compare the performance of Weibull regression distribution. For the simulation comparison, the study used various sample sizes and several specific values of the Weibull shape parameter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=weibull%20regression%20distribution" title="weibull regression distribution">weibull regression distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimator" title=" maximum likelihood estimator"> maximum likelihood estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20function" title=" survival function"> survival function</a>, <a href="https://publications.waset.org/abstracts/search?q=hazard%20rate" title=" hazard rate"> hazard rate</a>, <a href="https://publications.waset.org/abstracts/search?q=right%20censoring" title=" right censoring"> right censoring</a> </p> <a href="https://publications.waset.org/abstracts/40164/survival-and-hazard-maximum-likelihood-estimator-with-covariate-based-on-right-censored-data-of-weibull-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40164.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">441</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">1603</span> Analyzing Large Scale Recurrent Event Data with a Divide-And-Conquer Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jerry%20Q.%20Cheng">Jerry Q. Cheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title="big data analytics">big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=divide-and-conquer" title=" divide-and-conquer"> divide-and-conquer</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20event%20data" title=" recurrent event data"> recurrent event data</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20computing" title=" statistical computing"> statistical computing</a> </p> <a href="https://publications.waset.org/abstracts/100777/analyzing-large-scale-recurrent-event-data-with-a-divide-and-conquer-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100777.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">165</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">1602</span> Developing Variable Repetitive Group Sampling Control Chart Using Regression Estimator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liaquat%20Ahmad">Liaquat Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Aslam"> Muhammad Aslam</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Azam"> Muhammad Azam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we propose a control chart based on repetitive group sampling scheme for the location parameter. This charting scheme is based on the regression estimator; an estimator that capitalize the relationship between the variables of interest to provide more sensitive control than the commonly used individual variables. The control limit coefficients have been estimated for different sample sizes for less and highly correlated variables. The monitoring of the production process is constructed by adopting the procedure of the Shewhart’s x-bar control chart. Its performance is verified by the average run length calculations when the shift occurs in the average value of the estimator. It has been observed that the less correlated variables have rapid false alarm rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title=" control charts"> control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20shift" title=" process shift"> process shift</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20estimators" title=" regression estimators"> regression estimators</a>, <a href="https://publications.waset.org/abstracts/search?q=repetitive%20group%20sampling" title=" repetitive group sampling"> repetitive group sampling</a> </p> <a href="https://publications.waset.org/abstracts/13539/developing-variable-repetitive-group-sampling-control-chart-using-regression-estimator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13539.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">565</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">1601</span> Linear Quadratic Gaussian/Loop Transfer Recover Control Flight Control on a Nonlinear Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Sanches">T. Sanches</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Bousson"> K. Bousson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As part of the development of a 4D autopilot system for unmanned aerial vehicles (UAVs), i.e. a time-dependent robust trajectory generation and control algorithm, this work addresses the problem of optimal path control based on the flight sensors data output that may be unreliable due to noise on data acquisition and/or transmission under certain circumstances. Although several filtering methods, such as the Kalman-Bucy filter or the Linear Quadratic Gaussian/Loop Transfer Recover Control (LQG/LTR), are available, the utter complexity of the control system, together with the robustness and reliability required of such a system on a UAV for airworthiness certifiable autonomous flight, required the development of a proper robust filter for a nonlinear system, as a way of further mitigate errors propagation to the control system and improve its ,performance. As such, a nonlinear algorithm based upon the LQG/LTR, is validated through computational simulation testing, is proposed on this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20flight" title="autonomous flight">autonomous flight</a>, <a href="https://publications.waset.org/abstracts/search?q=LQG%2FLTR" title=" LQG/LTR"> LQG/LTR</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20state%20estimator" title=" nonlinear state estimator"> nonlinear state estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20flight%20control" title=" robust flight control"> robust flight control</a> </p> <a href="https://publications.waset.org/abstracts/107546/linear-quadratic-gaussianloop-transfer-recover-control-flight-control-on-a-nonlinear-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107546.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">138</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">1600</span> Robust Variogram Fitting Using Non-Linear Rank-Based Estimators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hazem%20M.%20Al-Mofleh">Hazem M. Al-Mofleh</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20E.%20Daniels"> John E. Daniels</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20W.%20McKean"> Joseph W. McKean</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper numerous robust fitting procedures are considered in estimating spatial variograms. In spatial statistics, the conventional variogram fitting procedure (non-linear weighted least squares) suffers from the same outlier problem that has plagued this method from its inception. Even a 3-parameter model, like the variogram, can be adversely affected by a single outlier. This paper uses the Hogg-Type adaptive procedures to select an optimal score function for a rank-based estimator for these non-linear models. Numeric examples and simulation studies will demonstrate the robustness, utility, efficiency, and validity of these estimates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20relative%20efficiency" title="asymptotic relative efficiency">asymptotic relative efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=non-linear%20rank-based" title=" non-linear rank-based"> non-linear rank-based</a>, <a href="https://publications.waset.org/abstracts/search?q=rank%20estimates" title=" rank estimates"> rank estimates</a>, <a href="https://publications.waset.org/abstracts/search?q=variogram" title=" variogram"> variogram</a> </p> <a href="https://publications.waset.org/abstracts/43980/robust-variogram-fitting-using-non-linear-rank-based-estimators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43980.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">431</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">1599</span> Capture-recapture to Estimate Completeness of Pulmonary Tuberculosis with Two Sources</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ratchadaporn%20Ungcharoen">Ratchadaporn Ungcharoen</a>, <a href="https://publications.waset.org/abstracts/search?q=Lily%20Ingsrisawang"> Lily Ingsrisawang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Capture-recapture methods are popular techniques for indirect estimation the size of wildlife populations and the completeness of cases in epidemiology and social sciences. The aim of this study was to estimate the completeness of pulmonary tuberculosis cases confirmed by two sources of hospital registrations and surveillance systems in 2013 in Nakhon Pathom province, Thailand. Several estimators of population size were considered: the Lincoln-Petersen estimator, the Chapman estimator, the Chao’s lower bound estimator, the Zelterman’s estimator, etc. We focus on the Chapman and Chao’s lower bound estimators for estimating the completeness of pulmonary tuberculosis from two sources. The retrieved pulmonary tuberculosis data from two sources were analyzed and bootstrapped for 30 samples, with 241 observations from source 1 and 305 observations from source 2 per sample, for additional exploration of the completeness of pulmonary tuberculosis. The results from the original data show that the Chapman’s estimator gave the estimation of a total 360 (95% CI: 349-371) pulmonary tuberculosis cases, resulting in 57% estimated completeness cases. But the Chao’s lower bound estimator estimated the total of 365 (95% CI: 354-376) pulmonary tuberculosis cases and its estimated completeness cases was 55.9%. For the results from bootstrap samples, the Chapman and the Chao’s lower bound estimators gave an estimated 347 (95% CI: 309-385) and 353 (95% CI: 315-390) pulmonary tuberculosis cases, respectively. If for two sources recoding systems are available, record-linkage and capture-recapture analysis can be useful for estimating the completeness of different registration system. Both Chapman and Chao’s lower bound estimator approaches produce very close estimates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=capture-recapture" title="capture-recapture">capture-recapture</a>, <a href="https://publications.waset.org/abstracts/search?q=Chao" title=" Chao"> Chao</a>, <a href="https://publications.waset.org/abstracts/search?q=Chapman" title=" Chapman"> Chapman</a>, <a href="https://publications.waset.org/abstracts/search?q=pulmonary%20tuberculosis" title=" pulmonary tuberculosis"> pulmonary tuberculosis</a> </p> <a href="https://publications.waset.org/abstracts/23818/capture-recapture-to-estimate-completeness-of-pulmonary-tuberculosis-with-two-sources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23818.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">516</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">1598</span> Bayesian Estimation under Different Loss Functions Using Gamma Prior for the Case of Exponential Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md.%20Rashidul%20Hasan">Md. Rashidul Hasan</a>, <a href="https://publications.waset.org/abstracts/search?q=Atikur%20Rahman%20Baizid"> Atikur Rahman Baizid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Bayesian estimation approach is a non-classical estimation technique in statistical inference and is very useful in real world situation. The aim of this paper is to study the Bayes estimators of the parameter of exponential distribution under different loss functions and then compared among them as well as with the classical estimator named maximum likelihood estimator (MLE). In our real life, we always try to minimize the loss and we also want to gather some prior information (distribution) about the problem to solve it accurately. Here the gamma prior is used as the prior distribution of exponential distribution for finding the Bayes estimator. In our study, we also used different symmetric and asymmetric loss functions such as squared error loss function, quadratic loss function, modified linear exponential (MLINEX) loss function and non-linear exponential (NLINEX) loss function. Finally, mean square error (MSE) of the estimators are obtained and then presented graphically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayes%20estimator" title="Bayes estimator">Bayes estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimator%20%28MLE%29" title=" maximum likelihood estimator (MLE)"> maximum likelihood estimator (MLE)</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20linear%20exponential%20%28MLINEX%29%20loss%20function" title=" modified linear exponential (MLINEX) loss function"> modified linear exponential (MLINEX) loss function</a>, <a href="https://publications.waset.org/abstracts/search?q=Squared%20Error%20%28SE%29%20loss%20function" title=" Squared Error (SE) loss function"> Squared Error (SE) loss function</a>, <a href="https://publications.waset.org/abstracts/search?q=non-linear%20exponential%20%28NLINEX%29%20loss%20function" title=" non-linear exponential (NLINEX) loss function"> non-linear exponential (NLINEX) loss function</a> </p> <a href="https://publications.waset.org/abstracts/53902/bayesian-estimation-under-different-loss-functions-using-gamma-prior-for-the-case-of-exponential-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53902.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">384</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">1597</span> Estimation of Population Mean Using Characteristics of Poisson Distribution: An Application to Earthquake Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prayas%20Sharma">Prayas Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposed a generalized class of estimators, an exponential class of estimators based on the adaption of Sharma and Singh (2015) and Solanki and Singh (2013), and a simple difference estimator for estimating unknown population mean in the case of Poisson distributed population in simple random sampling without replacement. The expressions for mean square errors of the proposed classes of estimators are derived from the first order of approximation. It is shown that the adapted version of Solanki and Singh (2013), the exponential class of estimator, is always more efficient than the usual estimator, ratio, product, exponential ratio, and exponential product type estimators and equally efficient to simple difference estimator. Moreover, the adapted version of Sharma and Singh's (2015) estimator is always more efficient than all the estimators available in the literature. In addition, theoretical findings are supported by an empirical study to show the superiority of the constructed estimators over others with an application to earthquake data of Turkey. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auxiliary%20attribute" title="auxiliary attribute">auxiliary attribute</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20bi-serial" title=" point bi-serial"> point bi-serial</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20square%20error" title=" mean square error"> mean square error</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20random%20sampling" title=" simple random sampling"> simple random sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20distribution" title=" Poisson distribution"> Poisson distribution</a> </p> <a href="https://publications.waset.org/abstracts/171049/estimation-of-population-mean-using-characteristics-of-poisson-distribution-an-application-to-earthquake-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171049.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">155</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">1596</span> A Generalized Family of Estimators for Estimation of Unknown Population Variance in Simple Random Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saba%20Riaz">Saba Riaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20A.%20Hussain"> Syed A. Hussain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is addressing the estimation method of the unknown population variance of the variable of interest. A new generalized class of estimators of the finite population variance has been suggested using the auxiliary information. To improve the precision of the proposed class, known population variance of the auxiliary variable has been used. Mathematical expressions for the biases and the asymptotic variances of the suggested class are derived under large sample approximation. Theoretical and numerical comparisons are made to investigate the performances of the proposed class of estimators. The empirical study reveals that the suggested class of estimators performs better than the usual estimator, classical ratio estimator, classical product estimator and classical linear regression estimator. It has also been found that the suggested class of estimators is also more efficient than some recently published estimators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=study%20variable" title="study variable">study variable</a>, <a href="https://publications.waset.org/abstracts/search?q=auxiliary%20variable" title=" auxiliary variable"> auxiliary variable</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20population%20variance" title=" finite population variance"> finite population variance</a>, <a href="https://publications.waset.org/abstracts/search?q=bias" title=" bias"> bias</a>, <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20variance" title=" asymptotic variance"> asymptotic variance</a>, <a href="https://publications.waset.org/abstracts/search?q=percent%20relative%20efficiency" title=" percent relative efficiency"> percent relative efficiency</a> </p> <a href="https://publications.waset.org/abstracts/87115/a-generalized-family-of-estimators-for-estimation-of-unknown-population-variance-in-simple-random-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87115.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">225</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">1595</span> A New Method to Estimate the Low Income Proportion: Monte Carlo Simulations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Encarnaci%C3%B3n%20%C3%81lvarez">Encarnación Álvarez</a>, <a href="https://publications.waset.org/abstracts/search?q=Rosa%20M.%20Garc%C3%ADa-Fern%C3%A1ndez"> Rosa M. García-Fernández</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20F.%20Mu%C3%B1oz"> Juan F. Muñoz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Estimation of a proportion has many applications in economics and social studies. A common application is the estimation of the low income proportion, which gives the proportion of people classified as poor into a population. In this paper, we present this poverty indicator and propose to use the logistic regression estimator for the problem of estimating the low income proportion. Various sampling designs are presented. Assuming a real data set obtained from the European Survey on Income and Living Conditions, Monte Carlo simulation studies are carried out to analyze the empirical performance of the logistic regression estimator under the various sampling designs considered in this paper. Results derived from Monte Carlo simulation studies indicate that the logistic regression estimator can be more accurate than the customary estimator under the various sampling designs considered in this paper. The stratified sampling design can also provide more accurate results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=poverty%20line" title="poverty line">poverty line</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20of%20poverty" title=" risk of poverty"> risk of poverty</a>, <a href="https://publications.waset.org/abstracts/search?q=auxiliary%20variable" title=" auxiliary variable"> auxiliary variable</a>, <a href="https://publications.waset.org/abstracts/search?q=ratio%20method" title=" ratio method"> ratio method</a> </p> <a href="https://publications.waset.org/abstracts/8876/a-new-method-to-estimate-the-low-income-proportion-monte-carlo-simulations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8876.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">456</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">1594</span> Synthesis of the Robust Regulators on the Basis of the Criterion of the Maximum Stability Degree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Gayvoronsky">S. A. Gayvoronsky</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20A.%20Ezangina"> T. A. Ezangina </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The robust control system objects with interval-undermined parameters is considers in this paper. Initial information about the system is its characteristic polynomial with interval coefficients. On the basis of coefficient estimations of quality indices and criterion of the maximum stability degree, the methods of synthesis of a robust regulator parametric is developed. The example of the robust stabilization system synthesis of the rope tension is given in this article. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interval%20polynomial" title="interval polynomial">interval polynomial</a>, <a href="https://publications.waset.org/abstracts/search?q=controller%20synthesis" title=" controller synthesis"> controller synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=analysis%20of%20quality%20factors" title=" analysis of quality factors"> analysis of quality factors</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20degree%20of%20stability" title=" maximum degree of stability"> maximum degree of stability</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20degree%20of%20stability" title=" robust degree of stability"> robust degree of stability</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20oscillation" title=" robust oscillation"> robust oscillation</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20accuracy" title=" system accuracy "> system accuracy </a> </p> <a href="https://publications.waset.org/abstracts/2551/synthesis-of-the-robust-regulators-on-the-basis-of-the-criterion-of-the-maximum-stability-degree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2551.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> <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=robust%20estimator&page=2">2</a></li> <li class="page-item"><a class="page-link" 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