Hierarchical dynamic models for multivariate times series of counts

TitleHierarchical dynamic models for multivariate times series of counts
Publication TypeJournal Article
Year of Publication2014
AuthorsRavishanker, N, Serhiyenko, V, Willig, MR
JournalStatistics and Its Interface
Volume7
Issue2014
Pagination559-570
Accession NumberLUQ.1154
Other Numbers1154
KeywordsBayesian modeling, Ecology, gastropod abundance, nonlinear state space model
Abstract

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. Article can also be found in: http://hydrodictyon.eeb.uconn.edu/people/willig/Willig_pdf/SJ_196_Ravishanker_etal_2014.pdf

URLhttp://dx.doi.org/10.4310/SII.2014.v7.n4.a11
DOI10.4310/SII.2014.v7.n4.a11
Short TitleHierarchical dynamic models for multivariate times series of counts