Statistics and Its Interface

Volume 7 (2014)

Number 4

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



Nalini Ravishanker (Department of Statistics, University of Connecticut, Storrs, Conn., U.S.A.)

Volodymyr Serhiyenko (Department of Statistics, University of Connecticut, Storrs, Conn., U.S.A.)

Michael R. Willig (Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, Conn., U.S.A.)


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

Published 23 December 2014