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Bayesian subgroup analysis in regression using mixture models

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  • Im, Yunju
  • Tan, Aixin

Abstract

Heterogeneity occurs in many regression problems, where members from different latent subgroups respond differently to the covariates of interest (e.g., treatments) even after adjusting for other covariates. A Bayesian model called the mixture of finite mixtures (MFM) can be used to identify these subgroups, a key feature of which is that the number of subgroups is modeled as a random variable and its distribution is learned from the data. The Bayesian MFM model was not commonly used in earlier applications largely due to computational difficulties. In comparison, an alternative infinite mixture model called the Dirichlet Process Mixture (DPM) model has been a main Bayesian tool for clustering even though it is a mis-specified model for many applications. The popularity of DPM is partly due to its convenient mathematical properties that enable efficient computing algorithms.

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  • Im, Yunju & Tan, Aixin, 2021. "Bayesian subgroup analysis in regression using mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:csdana:v:162:y:2021:i:c:s0167947321000864
    DOI: 10.1016/j.csda.2021.107252
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    References listed on IDEAS

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    Cited by:

    1. Wang, Xin & Zhu, Zhengyuan & Zhang, Hao Helen, 2023. "Spatial heterogeneity automatic detection and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).

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