This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 526
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #306824
Title: Bayesian Models on Species Richness of Fish in the Gulf of Maine
Author(s): Xia Wang*+ and Ming-Hui Chen and Dipak K. Dey and Chiu-Yen Kuo
Companies: National Institute of Statistical Sciences and University of Connecticut and University of Connecticut and University of Connecticut
Address: 19 T.W. Alexander Drive, Research Triangle Park, NC, 27709,
Keywords: spatiotemporal model ; species richness ; zero inflated binomial distribution ; zero inflated Poisson distribution ; multivariate Bayesian Hierarchical model
Abstract:

The species problem is of particular importance in a wide range of disciplines including ecology, linguistics, genomics and proteomics. Imperfect detection, pseudo zero observations, sparse counts and uneven sampling intensity are common phenomena in these fields. We propose a multivariate Bayesian hierarchical model to deal with these challenges. The setup of zero inflated binomial and Poisson mixture model enables it to model heterogeneity in presence and abundance of species simultaneously. The hierarchical structure facilitates incorporating various sources of variation, such as factors related to location and time and between species correlation. Posterior statistical inference and prediction are readily available by Bayesian methodology. We apply this method to NEFSC survey data to study both the number and composition of fish species in the Gulf of Maine.


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