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.
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This work is supported by the National Social Science Foundation of China (Grant No. 16ZDA047), the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20191394), the National Natural Science Foundation of China (Grant No. 61672291) and the Key Research Project of Jiangsu Province of China (Grant No. 2019A005).
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Cao, C., He, M., Shi, J.Q. et al. Estimation and prediction of a generalized mixed-effects model with t-process for longitudinal correlated binary data. Comput Stat (2021). https://doi.org/10.1007/s00180-020-01057-0
- Functional data
- Heavy-tailed process