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Bayesian baseline-category logit random effects models for longitudinal nominal data

  • Kim, Jiyeong (Laboratory of Low Dose Risk Assessment, National Radiation Emergency Medical Center, Korea Institute of Radiological and Medical Sciences) ;
  • Lee, Keunbaik (Department of Statistics, Sungkyunkwan University)
  • Received : 2019.09.19
  • Accepted : 2019.12.24
  • Published : 2020.03.31

Abstract

Baseline-category logit random effects models have been used to analyze longitudinal nominal data. The models account for subject-specific variations using random effects. However, the random effects covariance matrix in the models needs to explain subject-specific variations as well as serial correlations for nominal outcomes. In order to satisfy them, the covariance matrix must be heterogeneous and high-dimensional. However, it is difficult to estimate the random effects covariance matrix due to its high dimensionality and positive-definiteness. In this paper, we exploit the modified Cholesky decomposition to estimate the high-dimensional heterogeneous random effects covariance matrix. Bayesian methodology is proposed to estimate parameters of interest. The proposed methods are illustrated with real data from the McKinney Homeless Research Project.

Acknowledgement

Supported by : National Research Foundation of Korea (KRF)

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