High-dimensional variable screening and bias in subsequent inference, with an empirical comparison

Open access
Date
2014-06Type
- Journal Article
Citations
Cited 33 times in
Web of Science
Cited 38 times in
Scopus
ETH Bibliography
yes
Altmetrics
Abstract
We review variable selection and variable screening in high-dimensional linear models. Thereby, a major focus is an empirical comparison of various estimation methods with respect to true and false positive selection rates based on 128 different sparse scenarios from semi-real data (real data covariables but synthetic regression coefficients and noise). Furthermore, we present some theoretical bounds for the bias in subsequent least squares estimation, using the selected variables from the first stage, which have direct implications for construction of p-values for regression coefficients. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000072111Publication status
publishedExternal links
Journal / series
Computational StatisticsVolume
Pages / Article No.
Publisher
SpringerSubject
Elastic net; Lasso; Linear model; Ridge; Sparsity; Sure independence screening; Variable selectionOrganisational unit
03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
Notes
It was possible to publish this article open access thanks to a Swiss National Licence with the publisher.More
Show all metadata
Citations
Cited 33 times in
Web of Science
Cited 38 times in
Scopus
ETH Bibliography
yes
Altmetrics