Abstract
Random effects in generalized linear mixed models (GLMM) are used to explain the serial correlation of the longitudinal categorical data. Because the covariance matrix is high dimensional and should be positive definite, its structure is assumed to be constant over subjects and to be restricted such as AR(1) structure. However, these assumptions are too strong and can result in biased estimates of the fixed effects. In this paper we propose a Bayesian modeling for the GLMM with regression models for parameters of the random effects covariance matrix using a moving average Cholesky decomposition which factors the covariance matrix into moving average (MA) parameters and IVs. We analyze lung cancer data using our proposed model.
| Original language | English |
|---|---|
| Pages (from-to) | 111-116 |
| Number of pages | 6 |
| Journal | Computational Statistics and Data Analysis |
| Volume | 80 |
| DOIs | |
| State | Published - Dec 2014 |
Bibliographical note
Funding Information:We would like to thank Dr. Myung-Ju Ahn of Samsung Medical Center in South Korea for providing the data and for their help in data collection and clarifying some issues with data. Keunbaik Lee’s work was supported by Basic Science Research Program through the National Research Foundation of Korea (KRF) funded by the Ministry of Education, Science and Technology ( NRF-2012R1A1A1004002 ). Jae Keun Yoo’s work was supported by Basic Science Research Program through the National Research Foundation of Korea (KRF) funded by the Ministry of Education, Science and Technology ( NRF-2012R1A1A1040077 ).
Keywords
- Cholesky decomposition
- Heterogeneity
- Longitudinal data