Ordinary least squares and Stein-rule predictions in regression models under inclusion of some superfluous variables

DOI10.1007/BF02926587zbMath0860.62055MaRDI QIDQ1815628

Yanyan Li

Publication date: 19 December 1996

Published in: Statistical Papers (Search for Journal in Brave)

zbMATH Keywords

ordinary least squaresexplanatory variablesStein-ruleefficiency properties of predictionsmisspecified linear regression model

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (2)

A comparison between two competing fixed parameter constrained general linear models with new regressors ⋮ Statistical analysis of a linear regression model with restrictions and superfluous variables

Cites Work

• A necessary and sufficient condition for the dominance of an improved family of estimators in linear regression models
• Pre-test procedures and forecasting in the regression model under restrictions
• Stein-rule estimator under inclusion of superfluous variables in linear regression models
• Properties of the ordinary least squares and stein-rule predictions in linear regression models with proxy variables

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