Large numbers of explanatory variables: a probabilistic assessment | Zendy

Heather Battey | Zendy; D. R. Cox | Zendy

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Large numbers of explanatory variables: a probabilistic assessment

Author(s) -

Heather Battey,

D. R. Cox

Publication year - 2018

Publication title -

proceedings of the royal society a mathematical physical and engineering sciences

Language(s) - English

Resource type - Journals

eISSN - 1471-2946

pISSN - 1364-5021

DOI - 10.1098/rspa.2017.0631

Subject(s) - regression analysis , statistical model , explanatory model , econometrics , probabilistic logic , key (lock) , statistics , mathematics , computer science , psychology , computer security

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.

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