A state space model for multivariate longitudinal count data

• 成果类型: Article
• 署名作者: Jorgensen, B; Lundbye-Christensen, S; Song, PXK; Sun, L
• 署名单位: University of Southern Denmark; Aalborg University; York University - Canada; University of British Columbia
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/86.1.169
• 发表日期: 1999
• 页码: 169181
• 关键词: time-series models; regression-models; air-pollution

摘要

We propose a nonstationary state space model for multivariate longitudinal count data driven by a latent gamma Markov process. The Poisson counts are assumed to be conditionally independent given the latent process, both over time and across categories. We consider a regression model where time-varying covariates may enter via either the Poisson model or the latent gamma process. Estimation is based on the Kalman smoother, and we consider analysis of residuals from both the Poisson model and the latent process. A reanalysis of Zeger's (1988) polio data shows that the choice between a stationary and nonstationary model is crucial for the correct assessment of the evidence of a long-term decrease in the rate of U.S. polio infection.