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A Bayes Inference for Ordinal Response with Latent Variable Approach

Author

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  • Naijun Sha

    (Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX 79968, USA
    Current address: 500 W University Ave., El Paso, TX 79968, USA.)

  • Benard Owusu Dechi

    (Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX 79968, USA)

Abstract

In this paper, we propose a Bayesian model for the analysis of categorical data with an ordered outcome. The method provides a latent variable approach with an informative prior transformed from a Dirichlet distribution for the boundary parameters. A simulation study is carried out to assess the performance of the methods under various settings of the data structure. Our method produces predictive accuracy over the conventional classification procedures. Real data are analyzed to demonstrate the efficiency of the proposed method.

Suggested Citation

  • Naijun Sha & Benard Owusu Dechi, 2019. "A Bayes Inference for Ordinal Response with Latent Variable Approach," Stats, MDPI, vol. 2(2), pages 1-11, June.
    • Handle: RePEc:gam:jstats:v:2:y:2019:i:2:p:23-331:d:240356
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    References listed on IDEAS

    as
    1. P. J. Brown & M. Vannucci & T. Fearn, 2002. "Bayes model averaging with selection of regressors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 519-536, August.
    2. Naijun Sha & Marina Vannucci & Mahlet G. Tadesse & Philip J. Brown & Ilaria Dragoni & Nick Davies & Tracy C. Roberts & Andrea Contestabile & Mike Salmon & Chris Buckley & Francesco Falciani, 2004. "Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage," Biometrics, The International Biometric Society, vol. 60(3), pages 812-819, September.
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    Cited by:

    1. Qi Zhang & Yihui Zhang & Yemao Xia, 2024. "Bayesian Feature Extraction for Two-Part Latent Variable Model with Polytomous Manifestations," Mathematics, MDPI, vol. 12(5), pages 1-23, March.
    2. Ejike R. Ugba & Daniel Mörlein & Jan Gertheiss, 2021. "Smoothing in Ordinal Regression: An Application to Sensory Data," Stats, MDPI, vol. 4(3), pages 1-18, July.
    3. Stefan Michael Stroka & Christian Heumann, 2024. "Is Anonymization Through Discretization Reliable? Modeling Latent Probability Distributions for Ordinal Data as a Solution to the Small Sample Size Problem," Stats, MDPI, vol. 7(4), pages 1-20, October.

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