Feng, Yang and Yu, Yi (2019) The restricted consistency property of leave-nv-out cross-validation for high-dimensional variable selection. Statistica Sinica, 29 . pp. 1607-1630. doi:10.5705/ss.202015.0394 ISSN 1017-0405.

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Abstract

Cross-validation (CV) methods are popular for selecting the tuning parameter in the high-dimensional variable selection problem. We show the misalignment of the CV is one possible reason of its over-selection behavior. To fix this issue, we propose a version of leave-nv-out cross-validation (CV(nv)), for selecting the optimal model among the restricted candidate model set for high-dimensional generalized linear models. By using the same candidate model sequence and a proper order of construction sample size nc in each CV split, CV(nv) avoids the potential hurdles in developing theoretical properties. CV(nv) is shown to enjoy the restricted model selection consistency property under mild conditions. Extensive simulations and real data analysis support the theoretical results and demonstrate the performances of CV(nv) in terms of both model selection and prediction.

Item Type:	Journal Article
Divisions:	Faculty of Science, Engineering and Medicine > Science > Statistics
Journal or Publication Title:	Statistica Sinica
Publisher:	Academia Sinica
ISSN:	1017-0405
Official Date:	March 2019
Dates:	Date Event March 2019 Published 22 February 2019 Available 18 March 2018 Accepted
Volume:	29
Page Range:	pp. 1607-1630
DOI:	10.5705/ss.202015.0394
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Related URLs:	Other Repository
Open Access Version:	ArXiv

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