Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/6993
Title: A new version of unbiased ridge regression estimator under the stochastic restricted linear regression model
Authors: Mustafa I. Alheety, B. M. GOLAM KIBRIA
Keywords: Mean squared error matrix;
Mixed estimator; Ridge
Issue Date: 2019
Publisher: Communications in Statistics - Simulation and Computation
Abstract: Crouse et al. (Commun Stat Theory Methods 24:2341–2354, 1995) proposed the unbiased ridge regression estimator for the multicollinear regression model. Jibo Wu (The Scientific World Journal; Volume 2014, Article ID 206943, 1–8) introduced an unbiased two-parameter estimator based on prior information and two-parameter estimator proposed by €Ozkale and Kacıranlar, 2007. A new version of unbiased two-parameter estimator for the stochastic restricted linear regression model is proposed in this paper. The properties and the performance of the proposed estimator compared to other common estimators using the mean squares error criterion for the goodness of fit have been studied. Finally; A numerical example and a simulation study has been given to illustrate the performance of the proposed estimator.
URI: http://localhost:8080/xmlui/handle/123456789/6993
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