Large numbers of explanatory variables: a probabilistic assessment

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Title: Large numbers of explanatory variables: a probabilistic assessment
Authors: Battey, HS
Cox, DR
Item 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.
Issue Date: 4-Jul-2018
Date of Acceptance: 4-Jun-2018
URI: http://hdl.handle.net/10044/1/60959
DOI: https://dx.doi.org/10.1098/rspa.2017.0631
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.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/P002757/1
EP/P002757/1
Keywords: 01 Mathematical Sciences
02 Physical Sciences
09 Engineering
Publication Status: Published
Article Number: 20170631
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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