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Bayesian Mixed Effects Model with Variable Selection

Author

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  • Mingan Yang

    (Division of Biostats and Epidemiology, San Diego State University, USA)

Abstract

Recently, many approaches have been proposed to address the problem of selecting both fixed and random effects in mixed effects models. In this article, we review several approaches by comparing their procedures and performances, discussing their similarities and differences, and explaining their advantages and disadvantages

Suggested Citation

  • Mingan Yang, 2020. "Bayesian Mixed Effects Model with Variable Selection," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 10(2), pages 27-29, August.
    • Handle: RePEc:adp:jbboaj:v:10:y:2020:i:2:p:27-29
    DOI: 10.19080/BBOAJ.2020.10.555782
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    References listed on IDEAS

    as
    1. Mingan Yang & Min Wang & Guanghui Dong, 2020. "Bayesian variable selection for mixed effects model with shrinkage prior," Computational Statistics, Springer, vol. 35(1), pages 227-243, March.
    2. Carlos M. Carvalho & Nicholas G. Polson & James G. Scott, 2010. "The horseshoe estimator for sparse signals," Biometrika, Biometrika Trust, vol. 97(2), pages 465-480.
    3. Satkartar K. Kinney & David B. Dunson, 2007. "Fixed and Random Effects Selection in Linear and Logistic Models," Biometrics, The International Biometric Society, vol. 63(3), pages 690-698, September.
    4. Yang, Mingan, 2012. "Bayesian variable selection for logistic mixed model with nonparametric random effects," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2663-2674.
    5. Zhen Chen & David B. Dunson, 2003. "Random Effects Selection in Linear Mixed Models," Biometrics, The International Biometric Society, vol. 59(4), pages 762-769, December.
    Full references (including those not matched with items on IDEAS)

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