Results 291 to 300 of about 25,397 (329)

Minimax Linear Regression Estimators With Application to Ridge Regression

Technometrics, 1982
This article considers minimax linear estimation of β in the multiple linear-regression model Y = Xβ + ξ. Some results from European publications are referenced and summarized and some new results are given. These minimax estimators of β can also be classified as ridgeregression estimators with nonstochastic ridge parameters.
Lawrence Peele, Thomas P. Ryan
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Mean squared error comparisons of the modified ridge regression estimator and iiie restricted ridge regression estimator

Communications in Statistics - Theory and Methods, 1998
Swindel (1976) introduced a modified ridge regression estimator based on prior information. Sarkar (1992) suggested a new estimator by combining in a particular way the two approaches followed in obtaining the restricted ieast squares and ordinary ndge regression estimators.
Kaçiranlar S., Sakallioglu S., Akdeniz F.   +2 more
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Linearized Restricted Ridge Regression Estimator in Linear Regression

Communications in Statistics - Theory and Methods, 2012
This 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, Feng Gao, Jian-Wen Xu
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New Ridge Regression Estimator in Semiparametric Regression Models

Communications in Statistics - Simulation and Computation, 2015
In 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 Empirical Bayes Ridge Regression Estimators Under Multicollinearity [PDF]

Communications in Statistics - Theory and Methods, 2004
In this paper, we consider the problem of estimating the regression parameters in a multiple linear regression model when the multicollinearity is present. Under the assumption of normality, we present three empirical Bayes estimators. One of them shrinks the least squares (LS) estimator towards the principal component. The second one is a hierarchical
Tatsuya Kubokawa, M. S. Srivastava
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Ridge Regression Estimation for Survey Samples

Communications in Statistics - Theory and Methods, 2008
This paper describes procedure for constructing a vector of regression weights. Under the regression superpopulation model, the ridge regression estimator that has minimum model mean squared error is derived. Through a simulation study, we compare the ridge regression weights, regression weights, quadratic programming weights, and raking ratio weights.
Mingue Park, Min Yang
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Bayes minimax ridge regression estimators

Communications in Statistics - Theory and Methods, 2018
ABSTRACTThe problem of estimating of the vector β of the linear regression model y = Aβ + ϵ with ϵ ∼ Np(0, σ2Ip) under quadratic loss function is considered when common variance σ2 is unknown.
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Beta ridge regression estimators: simulation and application

Communications in Statistics - Simulation and Computation, 2021
The beta regression model is commonly used when analyzing data that come in the form of rates or percentages.
Mohamed R. Abonazel, Ibrahim M. Taha
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Shrinkage Ridge Estimators in Linear Regression

Communications in Statistics - Simulation and Computation, 2013
The 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 ...
M. Arashi, S. M. M. Tabatabaey, M. Hassanzadeh Bashtian   +2 more
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Minimax Adaptive Generalized Ridge Regression Estimators

Journal of the American Statistical Association, 1978
Abstract We consider the problem of estimating the vector of regression coefficients of a linear model using generalized ridge regression estimators where the ridge constant is chosen on the basis of the data. For general quadratic loss we produce such estimators whose risk function dominates that of the least squares procedure provided the number of ...
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