title: BAYESIAN SPATIAL-TEMPORAL MODELING OF ECOLOGICAL ZERO-INFLATED COUNT DATA.
abstract: A Bayesian hierarchical model is developed for count data with spatial and temporal correlations as well as excessive zeros, uneven sampling intensities, and inference on missing spots. Our contribution is to develop a model on zero-inflated count data that provides flexibility in modeling spatial patterns in a dynamic manner and also improves the computational efficiency via dimension reduction. The proposed methodology is of particular importance for studying species presence and abundance in the field of ecological sciences. The proposed model is employed in the analysis of the survey data by the Northeast Fisheries Sciences Center (NEFSC) for estimation and prediction of the Atlantic cod in the Gulf of Maine - Georges Bank region. Model comparisons based on the deviance information criterion and the log predictive score show the improvement by the proposed spatial-temporal model.
authors.affiliation: Department of Mathematical Sciences, University of Cincinnati, 2815 Commons Way Cincinnati, OH 45221-0025, USA.
authors.forename: Xia
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Statistics, University of Connecticut, 215 Glenbrook Road, U-4120 Storrs, CT 06269-4120, U.S.A.
authors.forename: Ming-Hui
authors.initials: MH
authors.lastname: Chen
authors.affiliation: Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
authors.forename: Rita C
authors.initials: RC
authors.lastname: Kuo
authors.affiliation: Department of Statistics, University of Connecticut, 215 Glenbrook Road, U-4120 Storrs, CT 06269-4120, U.S.A.
authors.forename: Dipak K
authors.initials: DK
authors.lastname: Dey
keywords: Bayesian hierarchical modeling; deviance information criterion; log predictive score; spatial dynamic modeling; zero-inflated Poisson