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Consistent significance controlled variable selection in high‐dimensional regression
Author(s) -
Zambom Adriano Zanin,
Kim Jongwook
Publication year - 2018
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.210
Subject(s) - bonferroni correction , feature selection , selection (genetic algorithm) , variable (mathematics) , computer science , monte carlo method , set (abstract data type) , regression analysis , data set , regression , statistics , data mining , artificial intelligence , mathematics , machine learning , mathematical analysis , programming language
In regression analysis, selecting, out of a pool of available predictors, those that compose the true underlying data‐generating mechanism is a fundamental part of model building. This paper introduces a forward selection method that uses a novel entry criterium based on a combination of p‐values of the predictors already selected. Moreover, the proposed variable selection procedure controls the significance of all selected predictors at each step using False Discovery Rate corrections (or Bonferroni, or other correction criteria). Monte Carlo simulations suggest that the proposed method performs competitively against classical competitors. The proposed variable selection procedure is illustrated on a real data set.