Multinomial probit Bayesian additive regression trees. Issue 1 (4th April 2016)

• Record Type: Journal Article
• Authors: Kindo, Bereket P.; Wang, Hao; Peña, Edsel A.
• Is Part Of: Stat. Volume 5:Issue 1(2016)
• Page Start: 119
• Page End: 131
• Publication Date: 2016-04-04
• Subjects: Bayesian methods -- classification -- machine learning -- statistical computing; Statistics -- Periodicals; 519.2
• DOI: 10.1002/sta4.110
• Languages: English
• ISSNs: 2049-1573

Abstract

Abstract : This article proposes multinomial probit Bayesian additive regression trees (MPBART) as a multinomial probit extension of 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 with other discrete choice and multiclass classification methods. To implement MPBART, the R package mpbart is freely available from CRAN repositories. Copyright © 2016 John Wiley & Sons, Ltd.