Mixing least-squares estimators when the variance is unknown

DOI10.3150/08-BEJ135zbMath1168.62327arXiv0711.0372OpenAlexW2953012255MaRDI QIDQ1002537

Christophe Giraud

Publication date: 2 March 2009

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0711.0372

zbMATH Keywords

shrinkage estimatorlinear regressionoracle inequalitiesadaptive minimax estimationGibbs mixture

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05) Minimax procedures in statistical decision theory (62C20) Applications of functional analysis in probability theory and statistics (46N30)

Related Items

Sharp oracle inequalities for aggregation of affine estimators, Mirror averaging with sparsity priors, Estimator selection with respect to Hellinger-type risks, Aggregation of affine estimators, Estimation and variable selection with exponential weights, Optimal bounds for aggregation of affine estimators, Exponential screening and optimal rates of sparse estimation, Estimator selection in the Gaussian setting, High-dimensional regression with unknown variance, Sparse estimation by exponential weighting, Mixing least-squares estimators when the variance is unknown

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