Open access
Datum
2017-12Typ
- Journal Article
Abstract
We propose a residual and wild bootstrap methodology for individual and simultaneous inference in high-dimensional linear models with possibly non-Gaussian and heteroscedastic errors. We establish asymptotic consistency for simultaneous inference for parameters in groups G, where p≫n, s0=o(n1/2/{log(p)log(|G|)1/2}) and log(|G|)=o(n1/7), with p the number of variables, n the sample size and s0 the sparsity. The theory is complemented by many empirical results. Our proposed procedures are implemented in the R-package hdi (Meier et al. hdi: high-dimensional inference. R package version 0.1-6, 2016). Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000197723Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
TESTBand
Seiten / Artikelnummer
Verlag
SpringerThema
De-biased Lasso; De-sparsified Lasso; Gaussian approximation for maxima; High-dimensional linear model; Heteroscedastic errors; Multiple testing; Westfall–Young methodOrganisationseinheit
03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
Zugehörige Publikationen und Daten
Is referenced by: https://doi.org/10.3929/ethz-b-000205907
Anmerkungen
It was possible to publish this article open access thanks to a Swiss National Licence with the publisher.