Detangling robustness in high dimensions: composite versus model-averaged estimation

DOI10.1214/20-EJS1728zbMath1450.62087arXiv2006.07457MaRDI QIDQ2192312

Jelena Bradic, Jing Zhou, Gerda Claeskens

Publication date: 17 August 2020

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

Full work available at URL: https://arxiv.org/abs/2006.07457

zbMATH Keywords

linear regressioncomparison of performancesaveraged weighted estimation methodsweight uncertainty

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric robustness (62G35) Linear regression; mixed models (62J05)

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The robust desparsified lasso and the focused information criterion for high-dimensional generalized linear models, Automatic bias correction for testing in high‐dimensional linear models, A tradeoff between false discovery and true positive proportions for sparse high-dimensional logistic regression

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