Appropriate penalties in the final prediction error criterion: A decision theoretic approach

DOI10.1016/0167-7152(93)90212-2zbMath0788.62062OpenAlexW2089316176MaRDI QIDQ1314700

Abba M. Krieger, Ping Zhang

Publication date: 9 June 1994

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(93)90212-2

zbMATH Keywords

minimaxmodel selectionloss functionsignal to noise ratiopenaltyadmissibleresidual sum of squaresconstrained parameter spacefinal prediction error criterionFPE- criterionGaussian linear regression modelspost-selection risksquared prediction errorunbiased estimate of the error variance

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Admissibility in statistical decision theory (62C15)

Related Items (1)

Model selection criteria with data dependent penalty, with applications to data-driven neyman smooth tests

Cites Work

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• How Many Variables Should be Entered in a Regression Equation?
• How Biased is the Apparent Error Rate of a Prediction Rule?
• An optimal selection of regression variables

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