A ridge regression estimation approach to the measurement error model

JOURNAL OF MULTIVARIATE ANALYSIS

卷:123

Article

Saleh, A. K. Md Ehsanes¹  Shalabh² 

[1] Carleton Univ, Ottawa, ON K1S 5B6, Canada

[2] Indian Inst Technol, Dept Math & Stat, Kanpur 208016, Uttar Pradesh, India

关键词: Linear regression model;    Measurement error;    Multicollinearity;    Reliability matrix;    Ridge regression estimators;    Shrinkage estimation;    Stein type estimators;    Preliminary test estimator;   

DOI  :  10.1016/j.jmva.2013.08.014

来源: Elsevier

【 摘 要 】

This paper considers the estimation of the parameters of measurement error models where the estimated covariance matrix of the regression parameters is ill conditioned. We consider the Hoerl and Kennard type (1970) ridge regression (RR) modifications of the five quasi-empirical Bayes estimators of the regression parameters of a measurement error model when it is suspected that the parameters may belong to a linear subspace. The modifications are based on the estimated covariance matrix of the estimators of regression parameters. The estimators are compared and the dominance conditions as well as the regions of optimality of the proposed estimators are determined based on quadratic risks. (C) 2013 Elsevier Inc. All rights reserved.

【 授权许可】

Free