Classified mixed logistic model prediction

Hanmei Sun, Thuan Nguyen, Yihui Luan, Jiming Jiang

Research output: Contribution to journalArticle

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)63-74
Number of pages12
JournalJournal of Multivariate Analysis
Volume168
DOIs
StatePublished - Nov 1 2018

Keywords

  • Clustered binary data
  • CMLMP
  • CMMP
  • Matching
  • Mixed logistic model
  • Mixed model prediction
  • MSPE

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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