Improvements of the maximum pseudo-likelihood estimators in various spatial statistical models
Date
1999
Authors
Huang, Fuchun
Ogata, Yosihiko
Journal Title
Journal ISSN
Volume Title
Publisher
American Statistical Association
Abstract
Maximum pseudo-likelihood estimation has hitherto been viewed as a practical but flawed alternative to maximum likelihood estimation, necessary because the maximum likelihood estimator is too hard to compute, but flawed because of its inefficiency when the spatial interactions are strong. We demonstrate that a single Newton-Raphson step starting from the maximum pseudo-likelihood estimator produces an estimator which is close to the maximum likelihood estimator in terms of its actual value, attained likelihood, and efficiency, even in the presence of strong interactions. This hybrid technique greatly increases the practical applicability of pseudo-likelihood-based estimation. Additionally, in the case of the spatial point processes, we propose a proper maximum pseudo-likelihood estimator which is different from the conventional one. The proper maximum pseudo-likelihood estimator clearly shows better performance than the conventional one does when the spatial interactions are strong.
Description
Keywords
Keywords: Auto-normal model; Gibbs sampling; Ising model; Metropolis algorithm; Newton-Raphson transformation; Pairwise interacted point process
Citation
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Source
Journal of Computational and Graphical Statistics
Type
Journal article