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Linearized Ridge Regression Estimator in Linear Regression
Communications in Statistics - Theory and Methods, 2011In this article, we aim to study the linearized ridge regression (LRR) estimator in a linear regression model motivated by the work of Liu (1993). The LRR estimator and the two types of generalized Liu estimators are investigated under the PRESS criterion.
Xu-Qing Liu
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Linearized Restricted Ridge Regression Estimator in Linear Regression
Communications in Statistics - Theory and Methods, 2012This article primarily aims to put forward the linearized restricted ridge regression (LRRR) estimator in linear regression models. Two types of LRRR estimators are investigated under the PRESS criterion and the optimal LRRR estimators and the optimal restricted generalized ridge regression estimator are obtained.
Xu-Qing Liu
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MORE ON THE PRE-TEST ESTIMATOR IN RIDGE REGRESSION
Communications in Statistics - Theory and Methods, 2002ABSTRACT The problem of estimation of the regression coefficients in a multiple regression model is considered under a multicollinearity situation when it is suspected that the regression coefficients may be restricted to a subspace. The objective of this paper is to compare the usual preliminary test estimator and the preliminary test ridge regression
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Poisson regression diagnostics with ridge estimation
Communications in Statistics - Simulation and Computation, 2021Influential observations influence the Poisson regression model (PRM) inferences. There are the situations in the PRM, where the explanatory variables are correlated and influential observations oc...
Aamna Khan +2 more
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On the estimation of Bell regression model using ridge estimator
Communications in Statistics - Simulation and Computation, 2021The bell regression is used, when the response variable is in the form of counts with over dispersion.
Muhammad Amin +2 more
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Ridge Estimators in Logistic Regression
Applied Statistics, 1992Summary: In this paper it is shown how ridge estimators can be used in logistic regression to improve the parameter estimates and to diminish the error made by further predictions. Different ways to choose the unknown ridge parameter are discussed. The main attention focuses on ridge parameters obtained by cross-validation.
le Cessie, S., van Houwelingen, J. C.
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Beta ridge regression estimators: simulation and application
Communications in Statistics - Simulation and Computation, 2021The beta regression model is commonly used when analyzing data that come in the form of rates or percentages.
Mohamed Reda Abonazel, Ibrahim M. Taha
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Shrinkage Ridge Estimators in Linear Regression
Communications in Statistics - Simulation and Computation, 2013The problem of estimation of the regression coefficients in a multiple regression model (MRM) is considered under multicollinearity situation. Further it is suspected that the regression coefficients may be restricted to a subspace. In this approach, we present the estimators of the regression coefficients combining the idea of preliminary test ...
Mohammad Arashi +2 more
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New Ridge Regression Estimator in Semiparametric Regression Models
Communications in Statistics - Simulation and Computation, 2015In the context of ridge regression, the estimation of shrinkage parameter plays an important role in analyzing data. Many efforts have been put to develop the computation of risk function in different full-parametric ridge regression approaches using eigenvalues and then bringing an efficient estimator of shrinkage parameter based on them.
Mahdi Roozbeh, Mohammad Arashi
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Improved ridge regression estimators for the logistic regression model
Computational Statistics, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
A. K. Md. Ehsanes Saleh +1 more
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