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Classified mixed logistic model prediction

Author

Listed:
  • Sun, Hanmei
  • Nguyen, Thuan
  • Luan, Yihui
  • Jiang, Jiming

Abstract

We develop a classified mixed logistic model prediction (CMLMP) method for clustered binary data by extending a method proposed by Jiang et al. (2018) for continuous outcome data. By identifying a class, or cluster, that the new observations belong to, we are able to improve the prediction accuracy of a probabilistic mixed effect associated with a future observation over the traditional method of logistic regression and mixed model prediction without matching the class. Furthermore, we develop a new strategy for identifying the class for the new observations by utilizing covariates information, which improves accuracy of the class identification. In addition, we develop a method of obtaining second-order unbiased estimators of the mean squared prediction errors (MSPEs) for CMLMP, which are used to provide measures of uncertainty. We prove consistency of CMLMP, and demonstrate finite-sample performance of CMLMP via simulation studies. Our results show that the proposed CMLMP method outperforms the traditional methods in terms of predictive performance. An application to medical data is discussed.

Suggested Citation

  • Sun, Hanmei & Nguyen, Thuan & Luan, Yihui & Jiang, Jiming, 2018. "Classified mixed logistic model prediction," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 63-74.
    • Handle: RePEc:eee:jmvana:v:168:y:2018:i:c:p:63-74
    DOI: 10.1016/j.jmva.2018.06.004
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    References listed on IDEAS

    as
    1. Jiming Jiang & J. Sunil Rao & Jie Fan & Thuan Nguyen, 2018. "Classified Mixed Model Prediction," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 269-279, January.
    2. Karl W. Broman & Terence P. Speed, 2002. "A model selection approach for the identification of quantitative trait loci in experimental crosses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 641-656, October.
    3. Jiming Jiang & P. Lahiri, 2001. "Empirical Best Prediction for Small Area Inference with Binary Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(2), pages 217-243, June.
    Full references (including those not matched with items on IDEAS)

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