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  3. Soft Bayesian Additive Regression Trees (sbart) For Correlated Survey Response With Non-gaussian Error.
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  3. Soft Bayesian Additive Regression Trees (sbart) For Correlated Survey Response With Non-gaussian Error.

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Soft Bayesian Additive Regression Trees (SBART) for correlated survey response with non-Gaussian error.

Abhishek Mandal1, Antonio R Linero2, Dipankar Bandyopadhyay3

  • 1Department of Statistics, Florida State University, Tallahassee, FL, USA.

Journal of Nonparametric Statistics
|March 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces the Soft Bayesian Additive Regression Trees (SBART) framework for complex survey data. SBART enables quantile regression and modeling of skewed, heavy-tailed distributions in clustered survey data.

Keywords:
Markov chain Monte CarloSoft Bayesian Additive Regression Trees (SBART)complex surveypredictive distribution“Statistics” 62

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Complex survey data are vital across social sciences, public health, and market research.
  • Traditional regression methods struggle with unknown covariate effects, interactions, and non-Gaussian response distributions (heavy-tailed, skewed).
  • Nonparametric Bayesian regression, particularly for quantile regression and skewed clustered complex survey data, remains underexplored.

Purpose of the Study:

  • Introduce the Soft Bayesian Additive Regression Trees (SBART) framework.
  • Address limitations of parametric regression for complex survey data.
  • Provide a method for quantile regression and modeling skewed, heavy-tailed response distributions in clustered survey data with weights.

Main Methods:

  • Developed the Soft Bayesian Additive Regression Trees (SBART) framework.
  • Applied SBART to clustered survey data with subject-specific survey weights.
  • Utilized simulation studies and real-world data analysis (National Health and Nutrition Examination Survey).

    • Main Results:

    • SBART effectively performs quantile regression on complex survey data.
    • The framework successfully models heavy-tailed and skewed response distributions.
    • Demonstrated advantages of SBART through simulations and analysis of periodontal data.

    Conclusions:

    • SBART offers a robust nonparametric Bayesian approach for complex survey data analysis.
    • The method enhances modeling capabilities for quantile regression and skewed distributions.
    • SBART provides a valuable tool for researchers in public health, social sciences, and beyond.