Multiset Model Selection. Issue 2 (3rd April 2018)

• Record Type: Journal Article
• Authors: Hoegh, Andrew; Maiti, Dipayan; Leman, Scotland
• Is Part Of: Journal of computational and graphical statistics. Volume 27:Issue 2(2018)
• Page Start: 436
• Page End: 448
• Publication Date: 2018-04-03
• Subjects: Bayesian modeling -- MCMC -- Variable selection; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2017.1379408
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: Model selection algorithms are required to efficiently traverse the space of models. In problems with high-dimensional and possibly correlated covariates, efficient exploration of the model space becomes a challenge. To overcome this, a multiset is placed on the model space to enable efficient exploration of multiple model modes with minimal tuning. The multiset model selection (MSMS) framework is based on independent priors for the parameters and model indicators on variables. Posterior model probabilities can be easily obtained from multiset averaged posterior model probabilities in MSMS. The effectiveness of MSMS is demonstrated for linear and generalized linear models. Supplementary material for this article is available online.