High-dimensional variable screening and bias in subsequent inference, with an empirical comparison
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Date
2014-06
Publication Type
Journal Article
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yes
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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.
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published
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Journal / series
Volume
29 (3)
Pages / Article No.
407 - 430
Publisher
Springer
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Edition / version
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Software
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Date collected
Date created
Subject
Elastic net; Lasso; Linear model; Ridge; Sparsity; Sure independence screening; Variable selection
Organisational 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.