High-dimensional variable screening and bias in subsequent inference, with an empirical comparison
- Journal Article
Rights / licenseIn Copyright - Non-Commercial Use Permitted
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
Journal / seriesComputational Statistics
Pages / Article No.
SubjectElastic net; Lasso; Linear model; Ridge; Sparsity; Sure independence screening; Variable selection
Organisational unit03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
NotesIt was possible to publish this article open access thanks to a Swiss National Licence with the publisher.
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