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Bayesian semiparametric variable selection with applications to periodontal data.

Bo Cai1, Dipankar Bandyopadhyay2

  • 1Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, U.S.A.

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|February 23, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian method for selecting fixed and random effects in nonparametric models, addressing limitations of traditional normality assumptions in mixed-effects models for clustered and longitudinal data analysis.

Keywords:
centered latent variablesfixed and random effects selectionnonparametric Bayesprobit stick-breaking processstochastic search

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Linear mixed models commonly assume normality for random effects in clustered/longitudinal data.
  • This normality assumption can lead to bias, especially with variable selection.
  • Nonparametric assumptions may cause centering issues, complicating interpretation and variable selection.

Purpose of the Study:

  • To propose a novel Bayesian method for simultaneous fixed and random effects selection.
  • To address limitations of normality assumptions and centering problems in nonparametric random effects models.
  • To enable efficient and interpretable variable selection in complex data structures.

Main Methods:

  • Developed a Bayesian approach utilizing centered latent variables for regression coefficients.
  • Employed probit stick-breaking scale mixtures for latent variable distribution.
  • Incorporated mixture priors and covariance decomposition to resolve model issues.

Main Results:

  • The proposed method effectively handles variable selection for both fixed and random effects.
  • Demonstrated avoidance of centering problems inherent in flexible nonparametric models.
  • Outperformed competing methods in simulated and real-world data analyses.

Conclusions:

  • The Bayesian method offers a robust alternative for analyzing clustered and longitudinal data.
  • Provides accurate estimation and efficient variable selection without restrictive normality assumptions.
  • Applicable to various fields, including health studies like periodontal disease research.