Optimal equivariant prediction for high-dimensional linear models with arbitrary predictor covariance

DOI10.1214/13-EJS826zbMath1293.62154OpenAlexW2082347462MaRDI QIDQ358878

Lee H. Dicker

Publication date: 9 August 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1373461822

zbMATH Keywords

James-Stein estimatorridge regressionadaptive estimationoracle estimatorout-of-sample prediction

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Statistical decision theory (62C99)

Related Items (7)

Optimal equivariant prediction for high-dimensional linear models with arbitrary predictor covariance ⋮ Empirical Bayes estimates for a two-way cross-classified model ⋮ Conditional predictive inference for stable algorithms ⋮ From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation ⋮ High-dimensional asymptotics of prediction: ridge regression and classification ⋮ Ridge regression and asymptotic minimax estimation over spheres of growing dimension ⋮ High-dimensional linear models: a random matrix perspective

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