Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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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
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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.

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References

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