title: Multinomial probit Bayesian additive regression trees.
abstract: This article proposes multinomial probit Bayesian additive regression trees (MPBART) as a multinomial probit extension of BART - Bayesian additive regression trees. MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. Through two simulation studies and four real data examples, we show that MPBART exhibits very good predictive performance in comparison to other discrete choice and multiclass classification methods. To implement MPBART, theRpackagempbartis freely available from CRAN repositories.
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, South Carolina, 29208, USA.
authors.forename: Bereket P
authors.initials: BP
authors.lastname: Kindo
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, 48824, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, South Carolina, 29208, USA.
authors.forename: Edsel A
authors.initials: EA
authors.lastname: Peña
keywords: Bayesian methods; Classification; Machine learning; Statistical computing