A State Space Model for Multivariate Longitudinal Count Data
نویسنده
چکیده
A state space model for multivariate longitudinal count data driven by a latent gamma Markov process is proposed, the observed counts being conditionally independent and Poisson distributed given the latent process. We consider regression analysis for this model with time-varying covariates entering either via the Poisson model or via the latent gamma process. We develop the Kalman lter and smoother and investigate estimation based on the EM algorithm with the E-step approximated by the Kalman smoother. We also consider analysis of residuals from both the Poisson model and the gamma process.
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تاریخ انتشار 1998