Best-subset model selection based on multitudinal assessments of likelihood improvements.

Content Provider	Europe PMC
Author	Carter, Knute D. Cavanaugh, Joseph E.
Copyright Year	2019
Abstract	ABSTRACT A common model selection approach is to select the best model, according to some criterion, from among the collection of models defined by all possible subsets of the explanatory variables. Identifying an optimal subset has proven to be a challenging problem, both statistically and computationally. Our model selection procedure allows the researcher to nominate, a priori, the probability at which models containing false or spurious variables will be selected from among all possible subsets. The procedure determines whether inclusion of each candidate variable results in a sufficiently improved fitting term – and is hence named the SIFT procedure. Two variants are proposed: a naive method based on a set of restrictive assumptions and an empirical permutation-based method. Properties of these methods are investigated within the standard linear modeling framework and performance is evaluated against other model selection techniques. The SIFT procedure behaves as designed – asymptotically selecting variables that characterize the underlying data generating mechanism, while limiting selection of spurious variables to the desired level. The SIFT methodology offers researchers a promising new approach to model selection, providing the ability to control the probability of selecting a model that includes spurious variables to a level based on the context of the application.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9041924&blobtype=pdf
Page Count	37
ISSN	02664763
Volume Number	47
DOI	10.1080/02664763.2019.1645097
PubMed Central reference number	PMC9041924
Issue Number	13-15
PubMed reference number	35707408
Journal	Journal of Applied Statistics [J Appl Stat]
e-ISSN	13600532
Language	English
Publisher	Taylor & Francis
Publisher Date	2019-07-25
Access Restriction	Open
Rights License	© 2019 Informa UK Limited, trading as Taylor & Francis Group
Subject Keyword	Akaike information criterion Bayesian information criterion likelihood ratio linear models regression variable selection 62F07
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty