Two Classes of Almost Unbiased Type Principal Component Estimators in Linear Regression Model
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-02
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Yalian& Yang, Hu. 2014. Two Classes of Almost Unbiased Type Principal Component Estimators in Linear Regression Model. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-487291
Modern Language Association (MLA)
Li, Yalian& Yang, Hu. Two Classes of Almost Unbiased Type Principal Component Estimators in Linear Regression Model. Journal of Applied Mathematics No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-487291
American Medical Association (AMA)
Li, Yalian& Yang, Hu. Two Classes of Almost Unbiased Type Principal Component Estimators in Linear Regression Model. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-487291
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-487291