Stein-rule estimator under inclusion of superfluous variables in linear regression models

DOI10.1080/03610929108830618zbMath0900.62348OpenAlexW1993752461MaRDI QIDQ3135345

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Publication date: 17 October 1993

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929108830618

Mathematics Subject Classification ID

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

Related Items (3)

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

Cites Work

• Mean squared error matrix comparisons between biased estimators — An overview of recent results
• A mean square error test when stochastic restrictions are used in regression
• Linear Statistical Inference and its Applications
• Unnamed Item

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