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

Author

Listed:
  • 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
    Newcastle University)

  • Xin Liu

    (Nanjing University of Information Science and Technology)

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.

Suggested Citation

  • Chunzheng Cao & Ming He & Jian Qing Shi & Xin Liu, 2021. "Estimation and prediction of a generalized mixed-effects model with t-process for longitudinal correlated binary data," Computational Statistics, Springer, vol. 36(2), pages 1461-1479, June.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:2:d:10.1007_s00180-020-01057-0
    DOI: 10.1007/s00180-020-01057-0
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    References listed on IDEAS

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    1. Bo Wang & Jian Qing Shi, 2014. "Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1123-1133, September.
    2. Lu Cheng & Siddharth Ramchandran & Tommi Vatanen & Niina Lietzén & Riitta Lahesmaa & Aki Vehtari & Harri Lähdesmäki, 2019. "An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

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