A small-sample choice of the tuning parameter in ridge regression

DOI10.5705/SS.2013.284zbMath1415.62052OpenAlexW2312232310WikidataQ36683923 ScholiaQ36683923MaRDI QIDQ2950212

Philip S. Boonstra, Bhramar Mukherjee

Publication date: 8 October 2015

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4790465

zbMATH Keywords

cross-validationmarginal likelihoodAkaike's information criterionpenalized likelihoodgeneralized cross-validationhyperpenalty

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07)

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Shrinkage parameter selection via modified cross-validation approach for ridge regression model ⋮ A fast algorithm for optimizing ridge parameters in a generalized ridge regression by minimizing a model selection criterion

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