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| Format: | Artículo Open Access |
| Published: |
Wiley
2024
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| Online Access: | https://onlinelibrary.wiley.com/doi/10.1002/sta4.643 |
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Table of Contents:
- Ordered probit Bayesian additive regression trees for ordinal data Jaeyong Lee Beom Seuk Hwang Stat 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. 10.1002/sta4.643 http://onlinelibrary.wiley.com/termsAndConditions#vor