Automatic bias correction for testing in high‐dimensional linear models

Hypothesis testing is challenging due to the test statistic's complicated asymptotic distribution when it is based on a regularized estimator in high dimensions. We propose a robust testing framework for ℓ1$$ {\ell}_1 $$‐regularized M‐estimators to cope with non‐Gaussian distributed regression...

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Veröffentlicht in:	Statistica Neerlandica Jg. 77; H. 1; S. 71 - 98
Hauptverfasser:	Zhou, Jing, Claeskens, Gerda
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Blackwell Publishing Ltd 01.02.2023
Schlagworte:	Algorithms approximate message passing algorithm Bias confidence interval Confidence intervals high‐dimensional linear model hypothesis testing loss function Message passing Robustness (mathematics) Statistical analysis ℓ1$$ {\ell}_1 $$‐regularization
ISSN:	0039-0402, 1467-9574
Online-Zugang:	Volltext
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