Shrinkage Estimators for Prediction Out-of-Sample: Conditional Performance

DOI10.1080/03610926.2012.697968zbMath1347.62141arXiv1209.0899OpenAlexW2055009414MaRDI QIDQ4929186

Nina Huber, Hannes Leeb

Publication date: 13 June 2013

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

Full work available at URL: https://arxiv.org/abs/1209.0899

zbMATH Keywords

James-Stein estimatorrandom matrix theoryrandom design

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (3)

The relative effects of dimensionality and multiplicity of hypotheses on the \(F\)-test in linear regression ⋮ Optimal equivariant prediction for high-dimensional linear models with arbitrary predictor covariance ⋮ Conditional predictive inference for stable algorithms

Cites Work

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• Inadmissibility of maximum likelihood estimators in some multiple regression problems with three or more independent variables
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• An ancillarity paradox which appears in multiple linear regression

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