A simulation comparison on spatial Poisson mixed models

ABSTRACT

The statistical methods for analyzing spatial count data have often been based on random fields so that a latent variable can be used to specify the spatial dependence. In this article, we introduce two frequentist approaches for estimating the parameters of model-based spatial count variables. The comparison has been carried out by a simulation study. The performance is also evaluated using a real dataset and also by the simulation study. The simulation results show that the maximum likelihood estimator appears to be with the better sampling properties.

KEYWORDS:

• Generalized linear mixed model
• Maximum likelihood estimation
• MCMC algorithms
• Spatial count data

MATHEMATICS SUBJECT CLASSIFICATION:

• 62H11
• 62J12