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- Title
Ordered probit Bayesian additive regression trees for ordinal data.
- Authors
Lee, Jaeyong; Hwang, Beom Seuk
- Abstract
Bayesian additive regression trees (BART) is a nonparametric model that is known for its flexibility and strong statistical foundation. To address a robust and flexible approach to analyse ordinal data, we extend BART into an ordered probit regression framework (OPBART). Further, we propose a semiparametric setting for OPBART (semi‐OPBART) to model covariates of interest parametrically and confounding variables nonparametrically. We also provide Gibbs sampling procedures to implement the proposed models. In both simulations and real data studies, the proposed models demonstrate superior performance over other competing ordinal models. We also highlight enhanced interpretability of semi‐OPBART in terms of inference through marginal effects.
- Subjects
GIBBS sampling; SAMPLING (Process); CONFOUNDING variables; PROBIT analysis; REGRESSION trees
- Publication
Stat, 2024, Vol 13, Issue 1, p1
- ISSN
2049-1573
- Publication type
Article
- DOI
10.1002/sta4.643