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

• Published in: Communications for statistical applications and methods Vol. 27; no. 2; pp. 201 - 210
• Main Authors: Jiyeong Kim, Keunbaik Lee
• Format: Journal Article
• Language: English
• Published: 한국통계학회 01.03.2020
• Subjects: 통계학; high-dimensional; positive-definiteness; heterogeneous; covariance matrix; modified Cholesky decomposition
• ISSN: 2287-7843; 2383-4757; 2383-4757
• DOI: 10.29220/CSAM.2020.27.2.201

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. KCI Citation Count: 0