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...

Full description

Bibliographic Details

Published in:	Statistica Neerlandica Vol. 77; no. 1; pp. 71 - 98
Main Authors:	Zhou, Jing, Claeskens, Gerda
Format:	Journal Article
Language:	English
Published:	Oxford Blackwell Publishing Ltd 01.02.2023
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!