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Activity Number:
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174
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #307724 |
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Title:
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On Least-Squares Fitting for Spatial Point Processes
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Author(s):
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Michael Sherman*+ and Yongtao Guan
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Companies:
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Texas A&M University and Yale University
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Address:
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Dept. of Statistics, College station, TX, 77843-3143,
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Keywords:
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K-function ; least squares estimator ; subsampling ; spatial point process
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Abstract:
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The K-function is a popular tool to fit spatial point process models due to its simplicity and applicability to a wide variety of point process models. In this work we study the properties of least squares estimators of model parameters and propose a new method of model fitting via the K-function using subsampling. We demonstrate consistency and asymptotic normality of our estimators of model parameters and compare the efficiency of our procedure with existing ones. This is done through asymptotic theory, simulation experiments and an application to a data set on long leaf pine trees.
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