Bibliographic Details
| Title: |
Semiparametric mixed‐scale models using shared Bayesian forests |
| Authors: |
Linero, Antonio R., Sinha, Debajyoti, Lipsitz, Stuart R. |
| Contributors: |
Division of Mathematical Sciences |
| Superior Title: |
Biometrics ; volume 76, issue 1, page 131-144 ; ISSN 0006-341X 1541-0420 |
| Publisher Information: |
Wiley |
| Publication Year: |
2019 |
| Collection: |
Wiley Online Library (Open Access Articles via Crossref) |
| Subject Terms: |
Applied Mathematics, General Agricultural and Biological Sciences, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, General Medicine, Statistics and Probability |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1111/biom.13107 |
| Availability: |
https://doi.org/10.1111/biom.13107 |
| Rights: |
http://onlinelibrary.wiley.com/termsAndConditions#am ; http://onlinelibrary.wiley.com/termsAndConditions#vor |
| Accession Number: |
edsbas.BFE7F768 |
| Database: |
BASE |
