Bayesian baseline-category logit random effects models for longitudinal nominal dataopen access
- Authors
- Kim, Jiyeong; Lee, Keunbaik
- Issue Date
- Mar-2020
- Publisher
- Korean Statistical SocietyRoom 803, 88, Gasan digital 1-ro, Geumcheon-guSeoul08590office@kss.or.kr
- Keywords
- covariance matrix; heterogeneous; high-dimensional; modified Cholesky decomposition; positive-definiteness
- Citation
- Communications for Statistical Applications and Methods, v.27, no.2, pp.201 - 210
- Indexed
- SCOPUS
KCI
- Journal Title
- Communications for Statistical Applications and Methods
- Volume
- 27
- Number
- 2
- Start Page
- 201
- End Page
- 210
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/192240
- DOI
- 10.29220/CSAM.2020.27.2.201
- ISSN
- 2287-7843
- 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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