Multinomial probit Bayesian additive regression trees | Zendy

Kindo Bereket P. | Zendy; Wang Hao | Zendy; Peña Edsel A. | Zendy

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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 , probit model , bayesian probability , multinomial distribution , probit , multivariate probit model , econometrics , ordered probit , statistics , computer science , regression , regression analysis , bayesian linear regression , mathematics , bayesian inference

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.

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