Paper Info
Title
Hierarchical Poisson models for spatial count data
Abstract
This work proposes a class of hierarchical models for geostatistical count data that includes the model proposed by Diggle et al. (1998) [13] as a particular case. For this class of models the main second-order properties of the count variables are derived, and three models within this class are studied in some detail. It is shown that for this class of models there is a close connection between the correlation structure of the counts and their overdispersions, and this property can be used to explore the flexibility of the correlation structures of these models. It is suggested that the models in this class may not be adequate to represent data consisting mostly of small counts with substantial spatial correlation. Three geostatistical count datasets are used to illustrate these issues and suggest how the results might be used to guide the selection of a model within this class.
Year
DOI
Venue
2013
10.1016/j.jmva.2013.08.015
J. Multivariate Analysis
Keywords
Field
DocType
particular case,spatial count data,correlation structure,count variable,small count,geostatistical count datasets,main second-order property,close connection,hierarchical poisson model,substantial spatial correlation,hierarchical model,geostatistical count data,generalized linear mixed model,geostatistics,gaussian random field,copula
Econometrics,Quasi-likelihood,Spatial correlation,Gaussian random field,Copula (linguistics),Count data,Poisson distribution,Generalized linear mixed model,Statistics,Geostatistics,Mathematics
Journal
Volume
ISSN
Citations 
122,
0047-259X
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Victor De Oliveira111.83
victor281.70