This paper demonstrates the advantages of sharing information about unknown features of covariates across multiple model components in various nonparametric regression problems including multivariate, heteroscedastic, and semicontinuous responses. In this paper, we present a methodology which allows for information to be shared nonparametrically across various model components using Bayesian sum‐of‐tree models. Our simulation results demonstrate that sharing of information across related model components is often very beneficial, particularly in sparse high‐dimensional problems in which variable selection must be conducted. We illustrate our methodology by analyzing medical expenditure data from the Medical Expenditure Panel Survey (MEPS). To facilitate the Bayesian nonparametric regression analysis, we develop two novel models for analyzing the MEPS data using Bayesian additive regression trees—a heteroskedastic log‐normal hurdle model with a “shrink‐toward‐homoskedasticity” prior and a gamma hurdle model.
- Award ID(s):
- 1712870
- NSF-PAR ID:
- 10097145
- Date Published:
- Journal Name:
- Proceedings of the 36th International Conference on Machine Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
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Accepted Manuscript1.0
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