Estimation and prediction of a generalized mixed-effects model with t-process for longitudinal correlated binary data

Chunzheng Cao, Ming He, Jian Qing Shi () and Xin Liu
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Chunzheng Cao: Nanjing University of Information Science and Technology
Ming He: Nanjing University of Information Science and Technology
Jian Qing Shi: Southern University of Science and Technology
Xin Liu: Nanjing University of Information Science and Technology

Computational Statistics, 2021, vol. 36, issue 2, No 29, 1479 pages

Abstract: Abstract We propose a generalized mixed-effects model based on t-process for longitudinal correlated binary data. The correlations among repeated binary outcomes are defined by a latent t-process, which provides a new framework on modeling nonlinear random- effects. The covariance kernel of the process can adaptively capture the subject-specific variations while the heavy-tails of the t-process enable robust inferences. We develop an efficient estimation procedure based on Monte Carlo EM algorithm and a prediction approach through conditional inference. Numerical studies indicate that the estimation and prediction based on the proposed model is robust against outliers compared with Gaussian model. We use the renal anemia and meteorological data as illustrative examples.

Keywords: Functional data; Heavy-tailed process; Prediction; Random-effects; Robustness (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00180-020-01057-0

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