Improved prediction in the presence of multicollinearity
type: Item
instance of: scholarly article
title: Improved prediction in the presence of multicollinearity
zbMATH Open document ID: 0617.62054
DOI: 10.1016/0304-4076(87)90082-0
author: R. Carter Hill
author: George G. Judge
published in: Journal of Econometrics
publication date: 1987-01-01T00:00:00Z
review text: Monte Carlo techniques are used to examine the performance of several estimators for the linear statistical model under a squared error of prediction loss measure when the data are multicollinear. Under this measure of performance the Stein-like rules that shrink toward the principal components estimator perform very well relative to other minimax estimators for alternative specifications of the characteristic root spectrum. The sampling performance of a non-minimax pretest rule is also considered.
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62J07
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 65C99
zbMATH DE Number: 4001221
zbMATH Keywords: comparison of estimators
zbMATH Keywords: Mundlak's pre-test estimator
zbMATH Keywords: James-Stein estimator
zbMATH Keywords: partitioned limited translation estimators
zbMATH Keywords: Monte Carlo experiment
zbMATH Keywords: multicollinearity
zbMATH Keywords: squared error of prediction loss
zbMATH Keywords: Stein- like rules
zbMATH Keywords: principal components estimator
zbMATH Keywords: minimax estimators
zbMATH Keywords: characteristic root spectrum
zbMATH Keywords: sampling performance
zbMATH Keywords: non-minimax pretest rule
MaRDI profile type: Publication
full work available at URL: 0304-4076(87)90082-0
OpenAlex ID: W2041268520
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description: scientific article; zbMATH DE number 4001221