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  1. Journal of Agricultural, Biological, and Environmental Statistics
  2. Journal of Agricultural, Biological, and Environmental Statistics : Volume 18
  3. Journal of Agricultural, Biological, and Environmental Statistics : Volume 18, Issue 3, September 2013
  4. Hierarchical Bayesian Spatio-Temporal Conway–Maxwell Poisson Models with Dynamic Dispersion
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Journal of Agricultural, Biological, and Environmental Statistics : Volume 22
Journal of Agricultural, Biological, and Environmental Statistics : Volume 21
Journal of Agricultural, Biological, and Environmental Statistics : Volume 20
Journal of Agricultural, Biological, and Environmental Statistics : Volume 19
Journal of Agricultural, Biological, and Environmental Statistics : Volume 18
Journal of Agricultural, Biological, and Environmental Statistics : Volume 18, Issue 4, December 2013
Journal of Agricultural, Biological, and Environmental Statistics : Volume 18, Issue 3, September 2013
Guest Editor’s Introduction to the Special Issue on “Modern Dimension Reduction Methods for Big Data Problems in Ecology”
Modeling Complex Spatial Dependencies: Low-Rank Spatially Varying Cross-Covariances With Application to Soil Nutrient Data
Bayesian Clustering of Animal Abundance Trends for Inference and Dimension Reduction
Spatial Regression Modeling for Compositional Data With Many Zeros
Hierarchical Bayesian Spatio-Temporal Conway–Maxwell Poisson Models with Dynamic Dispersion
Finite Mixture of Regression Modeling for High-Dimensional Count and Biomass Data in Ecology
Dynamic Inverse Prediction and Sensitivity Analysis With High-Dimensional Responses: Application to Climate-Change Vulnerability of Biodiversity
Computationally Efficient Statistical Differential Equation Modeling Using Homogenization
On Estimation and Selection of Autologistic Regression Models via Penalized Pseudolikelihood
Ecological Prediction With Nonlinear Multivariate Time-Frequency Functional Data Models
Journal of Agricultural, Biological, and Environmental Statistics : Volume 18, Issue 2, June 2013
Journal of Agricultural, Biological, and Environmental Statistics : Volume 18, Issue 1, March 2013
Journal of Agricultural, Biological, and Environmental Statistics : Volume 17
Journal of Agricultural, Biological, and Environmental Statistics : Volume 16
Journal of Agricultural, Biological, and Environmental Statistics : Volume 15
Journal of Agricultural, Biological, and Environmental Statistics : Volume 14
Journal of Agricultural, Biological, and Environmental Statistics : Volume 13
Journal of Agricultural, Biological, and Environmental Statistics : Volume 12
Journal of Agricultural, Biological, and Environmental Statistics : Volume 11
Journal of Agricultural, Biological, and Environmental Statistics : Volume 10
Journal of Agricultural, Biological, and Environmental Statistics : Volume 9
Journal of Agricultural, Biological, and Environmental Statistics : Volume 8
Journal of Agricultural, Biological, and Environmental Statistics : Volume 7
Journal of Agricultural, Biological, and Environmental Statistics : Volume 6

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Hierarchical Bayesian Spatio-Temporal Conway–Maxwell Poisson Models with Dynamic Dispersion

Content Provider Springer Nature Link
Author Wu, Guohui Holan, Scott H. Wikle, Christopher K.
Copyright Year 2013
Abstract Modeling spatio-temporal count processes is often a challenging endeavor. That is, in many real-world applications the complexity and high-dimensionality of the data and/or process do not allow for routine model specification. For example, spatio-temporal count data often exhibit temporally varying over/underdispersion within the spatial domain. In order to accommodate such structure, while quantifying different sources of uncertainty, we propose a Bayesian spatio-temporal Conway–Maxwell Poisson (CMP) model with dynamic dispersion. Motivated by the problem of predicting migratory bird settling patterns, we propose a threshold vector-autoregressive model for the CMP intensity parameter that allows for regime switching based on climate conditions. Additionally, to reduce the inherent high-dimensionality of the underlying process, we consider nonlinear dimension reduction through kernel principal component analysis. Finally, we demonstrate the effectiveness of our approach through out-of-sample one-year-ahead prediction of waterfowl migratory patterns across the United States and Canada. The proposed approach is of independent interest and illustrates the potential benefits of dynamic dispersion in terms of superior forecasting.This article has supplementary material online.
Starting Page 335
Ending Page 356
Page Count 22
File Format PDF
ISSN 10857117
Journal Journal of Agricultural, Biological, and Environmental Statistics
Volume Number 18
Issue Number 3
e-ISSN 15372693
Language English
Publisher Springer US
Publisher Date 2013-05-29
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Count data Empirical orthogonal functions Hierarchical model Kernel principal component analysis Nonlinear Overdispersion Threshold vector autoregressive model Underdispersion Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type Text
Resource Type Article
Subject Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty
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