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Comparison of Some Estimators under the Pitman’s Closeness Criterion in Linear Regression Model
Author(s) -
Jibo Wu
Publication year - 2014
Publication title -
journal of applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.307
H-Index - 43
eISSN - 1687-0042
pISSN - 1110-757X
DOI - 10.1155/2014/654949
Subject(s) - mean squared error , estimator , mathematics , algorithm , statistics , artificial intelligence , computer science
Batah et al. (2009) combined the unbiased ridge estimator and principal components regression estimator and introduced the modified r-k class estimator. They also showed that the modified r-k class estimator is superior to the ordinary least squares estimator and principal components regression estimator in the mean squared error matrix. In this paper, firstly, we will give a new method to obtain the modified r-k class estimator; secondly, we will discuss its properties in some detail, comparing the modified r-k class estimator to the ordinary least squares estimator and principal components regression estimator under the Pitman closeness criterion. A numerical example and a simulation study are given to illustrate our findings

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