Journal Article

A combined estimating function approach for fitting stationary point process models

C. Deng, R. P. Waagepetersen and Y. Guan

in Biometrika

Published on behalf of The Biometrika Trust

Volume 101, issue 2, pages 393-408
Published in print June 2014 | ISSN: 0006-3444
Published online March 2014 | e-ISSN: 1464-3510 | DOI: https://dx.doi.org/10.1093/biomet/ast069
A combined estimating function approach for fitting stationary point process models

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A composite likelihood technique based on pairwise contributions provides a computationally simple but potentially inefficient approach for fitting spatial point process models. We propose a new estimation procedure that improves the efficiency. Our approach combines estimating functions derived from pairwise composite likelihood estimation and estimating functions that account for correlations among the pairwise contributions. Our method can be used to fit a variety of parametric spatial point process models and can yield more efficient estimators for the clustering parameters than pairwise composite likelihood estimation. We demonstrate the efficacy of our proposed method through a simulation study and an application to the longleaf pine data.

Keywords: Estimating function; Pairwise composite likelihood; Spatial point process

Journal Article.  0 words. 

Subjects: Biomathematics and Statistics ; Probability and Statistics

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