Statistics and Its Interface
Volume 7 (2014)
Special Issue on Modern Bayesian Statistics (Part I)
Guest Editor: Ming-Hui Chen (University of Connecticut)
Hierarchical dynamic models for multivariate times series of counts
Pages: 559 – 570
In several application areas, we see the need for accurate statistical modeling of multivariate time series of counts as a function of relevant covariates. In ecology, count responses on species abundance are observed over several time periods at several locations, and the covariates that influence the abundance may be location-specific and/or time-varying. This paper describes a Bayesian framework for estimation and prediction by assuming a multivariate Poisson sampling distribution for the count responses and by fitting a hierarchical dynamic model. Our modeling incorporates the temporal dependence as well as dependence between the components of the response vector.
Bayesian modeling, ecology, gastropod abundance, nonlinear state space model