Title
Parameter estimation in the spatial auto-logistic model with working independent subblocks.
Abstract
We propose an approximation to the likelihood function with independent sub-blocks in the spatial auto-logistic model. The entire data is subdivided into many sub-blocks which are treated as independent from each other. The approximate maximum likelihood estimator, called maximum block independent likelihood estimator, is shown to have the same asymptotic distribution as that of the maximum likelihood estimator in the Ising model, a special case of the spatial auto-logistic model. The computational load for the proposed estimator is much lighter than that for the maximum likelihood estimator, and decreases geometrically as the size of a sub-block decreases. Also, limited simulation studies show that, in finite samples, the maximum block independent likelihood estimator performs as well as the maximum likelihood estimator in mean squared error. We apply our procedure to an estimation and a test of spatial dependence in the longleaf pine tree data in Cressie (1993) and the aerial image data in Pyun et al. (2007). Finally, we discuss the extension of the proposed estimator to other spatial auto-regressive models.
Year
DOI
Venue
2012
10.1016/j.csda.2012.03.013
Computational Statistics & Data Analysis
Keywords
Field
DocType
likelihood function,spatial auto-regressive model,maximum likelihood estimator,independent subblocks,parameter estimation,spatial dependence,spatial auto-logistic model,independent sub-blocks,maximum block,independent likelihood estimator,approximate maximum likelihood estimator,proposed estimator,composite likelihood
Econometrics,Efficient estimator,Minimum-variance unbiased estimator,Bias of an estimator,Estimation theory,Statistics,Restricted maximum likelihood,Bayes estimator,Mathematics,Consistent estimator,Estimator
Journal
Volume
Issue
ISSN
56
12
0167-9473
Citations 
PageRank 
References 
0
0.34
4
Authors
5
Name
Order
Citations
PageRank
Johan Lim16310.95
Kiseop Lee201.01
Donghyeon Yu332.10
Haiyan Liu400.34
Michael Sherman5153.81