Abstract Details
Activity Number:
|
21
|
Type:
|
Topic Contributed
|
Date/Time:
|
Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract - #310178 |
Title:
|
Bayesian Spatial-Temporal Modeling of Atlantic Cod Abundance in the Gulf of Maine
|
Author(s):
|
Xia Wang*+ and Ming-Hui Chen and Dipak K. Dey and Chiu-Yen Kou
|
Companies:
|
University of Cincinnati and University of Connecticut and University of Connecticut and University of Connecticut
|
Keywords:
|
Bayesian Hierarchical ;
Dynamic Factor Model ;
Predictive Process ;
Spatial-temproal model ;
Zero-inflated Poisson
|
Abstract:
|
This study aims at improving estimation and prediction of Atlantic Cod abundance in the Gulf of Maine using spatial-temporal models for zero inflated count data. We employ the Bayesian spatial dynamic factor model framework as proposed in Esther et al. (2011), which allows both flexibility in the model and dimension reduction. The proposed model not only models the spatial and temporal correlations in the variation in the abundance but also deals with the challenges posed by zero counts, uneven sampling intensities and inference on missing spots. The proposed model is employed in the analysis of the survey data by the Northeast Fisheries Sciences Center (NEFSC). Model comparisons show that the estimation and prediction is improved by the proposed spatial-temporal model.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.