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Multinomial probit Bayesian additive regression trees
Author(s) -
Kindo Bereket P.,
Wang Hao,
Peña Edsel A.
Publication year - 2016
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.110
Subject(s) - multinomial probit , multinomial logistic regression , bayesian probability , probit model , multinomial distribution , probit , multivariate probit model , econometrics , ordered probit , statistics , regression , computer science , regression analysis , mathematics
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.