Subsampling based variable selection for generalized linear models Journal Article
| Authors: | Capanu, M.; Giurcanu, M.; Begg, C. B.; Gönen, M. |
| Article Title: | Subsampling based variable selection for generalized linear models |
| Abstract: | A novel variable selection method for low-dimensional generalized linear models is introduced. The new approach called AIC OPTimization via STABility Selection (OPT-STABS) repeatedly subsamples the data, minimizes Akaike's Information Criterion (AIC) over a sequence of nested models for each subsample, and includes in the final model those predictors selected in the minimum AIC model in a large fraction of the subsamples. New methods are also introduced to establish an optimal variable selection cutoff over repeated subsamples. An extensive simulation study examining a variety of proposed variable selection methods shows that, although no single method uniformly outperforms the others in all the scenarios considered, OPT-STABS is consistently among the best-performing methods in most settings while it performs competitively for the rest. This is in contrast to other candidate methods which either have poor performance across the board or exhibit good performance in some settings, but very poor in others. In addition, the asymptotic properties of the OPT-STABS estimator are derived, and its root-n consistency and asymptotic normality are proved. The methods are applied to two datasets involving logistic and Poisson regressions. © 2023 Elsevier B.V. |
| Keywords: | computational methods; variable selection; aic; data handling; stability selection; new approaches; optimisations; screening threshold; subsampling; akaike's information criterions; generalized linear model; low dimensional; stability selections; variable selection methods; variables selections |
| Journal Title: | Computational Statistics and Data Analysis |
| Volume: | 184 |
| ISSN: | 0167-9473 |
| Publisher: | Elsevier B.V. |
| Date Published: | 2023-08-01 |
| Start Page: | 107740 |
| Language: | English |
| DOI: | 10.1016/j.csda.2023.107740 |
| PROVIDER: | scopus |
| PMCID: | PMC10118238 |
| PUBMED: | 37090139 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record and PDF. Corresponding author is MSK author Mithat Gönen -- Source: Scopus |
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