Large numbers of explanatory variables: a probabilistic assessment
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Published version
Author(s)
Battey, HS
Cox, DR
Type
Journal Article
Abstract
Recently, Cox and Battey (2017 Proc. Natl Acad. Sci. USA 114, 8592–8595 (doi:10.1073/pnas.1703764114)) outlined a procedure for regression analysis when there are a small number of study individuals and a large number of potential explanatory variables, but relatively few of the latter have a real effect. The present paper reports more formal statistical properties. The results are intended primarily to guide the choice of key tuning parameters.
Date Issued
2018-07-04
Date Acceptance
2018-06-04
Citation
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2018, 474 (2215)
ISSN
1364-5021
Publisher
Royal Society, The
Journal / Book Title
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume
474
Issue
2215
Copyright Statement
©2018 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License
http://creativecommons.org/licenses/by/4.0/
, which permits unrestricted use, provided the original author and source are credited.
http://creativecommons.org/licenses/by/4.0/
, which permits unrestricted use, provided the original author and source are credited.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/P002757/1
EP/P002757/1
Subjects
01 Mathematical Sciences
02 Physical Sciences
09 Engineering
Publication Status
Published
Article Number
20170631