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

Song Zhang, Ya Chen Tina Shihy, Peter Müullerz

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Scientic 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 infer-ence 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.

Original languageEnglish (US)
Pages (from-to)611-634
Number of pages24
JournalBayesian Analysis
Volume2
Issue number3
DOIs
StatePublished - Dec 1 2007

Keywords

  • Bart
  • Cart
  • Missing variables
  • Spatial model
  • Survey

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

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