NDLI: An alternative stochastic restricted Liu estimator in linear regression

Content Provider	Springer Nature Link
Author	Yang, Hu Xu, Jianwen
Copyright Year	2007
Abstract	In this paper, we introduce an alternative stochastic restricted Liu estimator for the vector of parameters in a linear regression model when additional stochastic linear restrictions on the parameter vector are assumed to hold. The new estimator is a generalization of the ordinary mixed estimator (OME) (Durbin in J Am Stat Assoc 48:799–808, 1953; Theil and Goldberger in Int Econ Rev 2:65–78, 1961; Theil in J Am Stat Assoc 58:401–414, 1963) and Liu estimator proposed by Liu (Commun Stat Theory Methods 22:393–402, 1993). Necessary and sufficient conditions for the superiority of the new stochastic restricted Liu estimator over the OME, the Liu estimator and the estimator proposed by Hubert and Wijekoon (Stat Pap 47:471–479, 2006) in the mean squared error matrix (MSEM) sense are derived. Furthermore, a numerical example based on the widely analysed dataset on Portland cement (Woods et al. in Ind Eng Chem 24:1207–1241, 1932) and a Monte Carlo evaluation of the estimators are also given to illustrate some of the theoretical results.
Starting Page	639
Ending Page	647
Page Count	9
File Format	PDF
ISSN	09325026
Journal	Statistical Papers
Volume Number	50
Issue Number	3
e-ISSN	16139798
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-10-31
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Liu estimator Mixed estimator Stochastic restricted Liu estimator Mean squared error matrix Operations Research/Decision Theory Economic Theory Probability Theory and Stochastic Processes Statistics for Business/Economics/Mathematical Finance/Insurance
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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