Two classes of almost unbiased type principal component estimators in linear regression model
type: Item
instance of: scholarly article
title: Two classes of almost unbiased type principal component estimators in linear regression model
zbMATH Open document ID: 1407.62259
DOI: 10.1155/2014/639070
author: Yalian Li
author: Hu Yang
published in: Journal of Applied Mathematics
publication date: 2019-02-01T00:00:00Z
review text: Summary: This paper is concerned with the parameter estimator in linear regression model. To overcome the multicollinearity problem, two new classes of estimators called the almost unbiased ridge-type principal component estimator (AURPCE) and the almost unbiased Liu-type principal component estimator (AULPCE) are proposed, respectively. The mean squared error matrix of the proposed estimators is derived and compared, and some properties of the proposed estimators are also discussed. Finally, a Monte Carlo simulation study is given to illustrate the performance of the proposed estimators.
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62H25
zbMATH DE Number: 7010705
Wikidata QID: Q59053899
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/639070
OpenAlex ID: W2163076985
DBLP publication ID: journals/jam/LiY14
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description: scientific article; zbMATH DE number 7010705