ArticleStatistics in medicine2023
Bayesian additive regression trees for multivariate skewed responses.
Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference.Statistics in medicine · 2026Article
- Soft Bayesian Additive Regression Trees (SBART) for correlated survey response with non-Gaussian error.Journal of nonparametric statistics · 2026Article
- Augmentation Samplers for Multinomial Probit Bayesian Additive Regression Trees.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Bayesian additive tree ensembles for composite quantile regressions.Statistics and computing · 2025Article
- Leveraging Machine Learning and Real-World Data to Predict Chronic Obstructive Pulmonary Disease Exacerbations.International journal of chronic obstructive pulmonary disease · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This paper introduces a nonparametric regression approach for univariate and multivariate skewed responses using Bayesian additive regression trees (BART). Existing BART methods use ensembles of decision trees to model a mean function, and have become popular recently due to their high prediction accuracy and ease of use. The usual assumption of a univariate Gaussian error distribution, however, is restrictive in many biomedical applications. Motivated by an oral health study, we provide a useful extension of BART, the skewBART model, to address this problem. We then extend skewBART to allow for multivariate responses, with information shared across the decision trees associated with different responses within the same subject. The methodology accommodates within-subject association, and allows varying skewness parameters for the varying multivariate responses. We illustrate the benefits of our multivariate skewBART proposal over existing alternatives via simulation studies and application to the oral health dataset with bivariate highly skewed responses. Our methodology is implementable via the R package skewBART, available on GitHub.
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Registered trials
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