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24 articles for “Regression Estimator”
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Research ArticleOpen accessPhrase in the titleA Modification of the Ridge Type Regression EstimatorsMoawad El-Fallah Abd El-SalamProblem statement: Many regression estimators have been used to remedy multicollinearity problem. The ridge estimator has been the most popular one. However, the obtained estimate is biased. Approach: In this stuyd, we introduce an alternative shrinkage estimator, called modified unbiased ridge (MUR) estimator for coping with multicollinearity problem. This estimator is obtained from Unbiased Ridge Regression (URR) in the same … …American Journal of Applied Sciences, Vol. 8, Issue 1 · 31 Jan 2011 · 7,638 accessesView PDFCite
Research ArticleOpen accessPhrase in the titlePERFORMANCE OF RIDGE REGRESSION ESTIMATOR METHODS ON SMALL SAMPLE SIZE BY VARYING CORRELATION COEFFICIENTS: A SIMULATION STUDYAnwar Fitrianto, Lee Ceng YikWhen independent variables have high linear correlation in a multiple linear regression model, we can have wrong analysis. It happens if we do the multiple linear regression analysis based on common Ordinary Least Squares (OLS) method. In this situation, we are suggested to use ridge regression estimator. We conduct some simulation study to compare the performance of ridge regression estimator … …Journal of Mathematics and Statistics, Vol. 10, Issue 1 · 3 Jan 2014 · 8,540 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSpline Estimator for Bi-Responses and Multi-Predictors Nonparametric Regression Model in Case of Longitudinal DataAdji Achmad Rinaldo Fernandes, I Nyoman Budiantara, Bambang Widjanarko Otok, SuhartonoNonparametric regression approach is used when the shape of the curve regression is unknown. The spline estimator approach for longitudinal data can accommodate the correlation between observations within the same subject, which is not found in the cross-section data, so that the autocorrelation assumption problem can be resolved. On the other hand, with bi-responses approach, it will accommodate any correlation … …Journal of Mathematics and Statistics, Vol. 11, Issue 2 · 8 Oct 2015 · 7,337 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSpline Estimator in Multi-Response Nonparametric Regression Model with Unequal Correlation of ErrorsBudi Lestari, I. Nyoman Budiantara, Sony Sunaryo, Muhammad MashuriProblem statement: In many applications two or more dependent variables are observed at several values of the independent variables, such as at time points. The statistical problems are to estimate functions that model their dependences on the independent variables and to investigate relationships between these functions. Nonparametric regression model, especially smoothing splines provide powerful tools to model the functions which … …Journal of Mathematics and Statistics, Vol. 6, Issue 3 · 30 Sep 2010 · 8,168 accessesView PDFCite
Research ArticleOpen accessIn the keywordsADAPTIVE CLUSTER SAMPLING USING AUXILIARY VARIABLENipaporn ChutimanIn this study we study the estimators of the population mean in adaptive cluster sampling by using the information of the auxiliary variable. The estimators in this study are the classical ratio estimator, the ratio estimator using the population coefficient of variation and the coefficient of kurtosis of the auxiliary variable, the regression estimator and the difference estimator. Simulations showed … …Journal of Mathematics and Statistics, Vol. 9, Issue 3 · 4 Sep 2013 · 9,894 accessesView PDFCite
Research ArticleOpen accessIn the keywordsThe Performance of Latent Root-M based RegressionHabshah Midi, Lau Ung HuaProblem statement: In the presence of multicollinearity, the estimation of parameters in multiple linear regression models by means of Ordinary Least Squares (OLS) is known to suffer severe distortion. An alternative approach was to use the modified OLS which was based on the latent roots and latent vectors of the correlation matrix of the independent and dependent variables. This procedure … …Journal of Mathematics and Statistics, Vol. 5, Issue 1 · 31 Mar 2009 · 8,654 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEffect of Multicollinearity on Power Rates of the Ordinary Least Squares EstimatorsO.O. Alabi, Kayode Ayinde, T.O. OlatayoInferences on the parameter estimates of Ordinary Least Square (OLS) estimator in regression model when regressors exhibit multicollinearity is a problem in that large standard errors of the regression coefficients which cause low t-statistic value often result into the acceptance of the null hypothesis. This paper, therefore, makes efforts to investigate the effect of multicollinearity on the power rates of … …Journal of Mathematics and Statistics, Vol. 4, Issue 2 · 30 Jun 2008 · 7,880 accessesView PDFCite
Research ArticleOpen accessIn the keywordsβ-Spline Estimation in a Semiparametric Regression Model with Nonlinear Time Series ErrorsJinhong You, Gemai Chen, Xian ZhouWe study the estimation problems for a partly linear regression model with a nonlinear time series error structure. The model consists of a parametric linear component for the regression coefficients and a nonparametric nonlinear component. The random errors are unobservable and modeled by a first-order Markov bilinear process. Based on a B-spline series approximation of the nonlinear component function, we … …American Journal of Applied Sciences, Vol. 2, Issue 9 · 30 Sep 2005 · 6,734 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparison of Collinearity Indices for Linear Models in Agricultural TrialsDanny Villegas Rivas, José M. Palacios Sánchez, Cristina A. Alzamora Rivero et al.The deleterious consequences of collinearity in linear regression on the precision of estimators of regression coefficients and the interpretability of the fitted model are widely recognized. In this study, we compare several methodologies for assessing collinearity in linear models and explore the effect of outliers on collinearity. The robustness of collinearity measures (individual and overall) is validated through two detailed … …OnLine Journal of Biological Sciences, Vol. 24, Issue 2 · 28 Dec 2023 · 6,491 accessesView PDFCite
Research ArticleOpen accessIn the abstractMODEL SELECTION VIA ROBUST VERSION OF R-SQUAREDShokrya SalehR-squared (R2) is a popular method for variable selection in linear regression models. R2 based on Least Squares (LS) regression minimizes the sum of the squared residuals; LS is sensitive to outlier observation. Alternative criterion based on M-estimators, which is less sensitive to outlying observation has been proposed. In this study explicit expression for such criterion is obtained when the … …Journal of Mathematics and Statistics, Vol. 10, Issue 3 · 9 Oct 2014 · 7,460 accessesView PDFCite