Bayesian Analysis

A spatially-adjusted Bayesian additive regression tree model to merge two datasets

Peter Müller, Ya-Chen Tina Shih, and Song Zhang

Full-text: Open access

Abstract

Scientific hypotheses of interest often involve variables that are not available in a single survey. This is a common problem for researchers working with survey data. We propose a model-based approach to provide information about the missing variable. We use a spatial extension of the BART (Bayesian additive regression tree) model. The imputation of the missing variables and inference about the relationship between two variables are obtained simultaneously as posterior inference under the proposed model. The uncertainty due to imputation is automatically accounted for. A simulation analysis and an application to data on self-perceived health status and income are presented.

Article information

Source
Bayesian Anal., Volume 2, Number 3 (2007), 611-633.

Dates
First available in Project Euclid: 22 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1340370729

Digital Object Identifier
doi:10.1214/07-BA224

Mathematical Reviews number (MathSciNet)
MR2342177

Zentralblatt MATH identifier
1331.62170

Keywords
BART CART Missing variables Spatial model Survey

Citation

Zhang, Song; Shih, Ya-Chen Tina; Müller, Peter. A spatially-adjusted Bayesian additive regression tree model to merge two datasets. Bayesian Anal. 2 (2007), no. 3, 611--633. doi:10.1214/07-BA224. https://projecteuclid.org/euclid.ba/1340370729


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