Moment estimation for statistics from marked point processes |
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| Authors: | Dimitris N. Politis,& Michael Sherman |
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| Affiliation: | University of California at San Diego, La Jolla, USA,;Texas A&M University, College Station, USA |
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| Abstract: | In spatial statistics the data typically consist of measurements of some quantity at irregularly scattered locations; in other words, the data form a realization of a marked point process. In this paper, we formulate subsampling estimators of the moments of general statistics computed from marked point process data, and we establish their L 2-consistency. The variance estimator in particular can be used for the construction of confidence intervals for estimated parameters. A practical data-based method for choosing a subsampling parameter is given and illustrated on a data set. Finite sample simulation examples are also presented. |
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| Keywords: | Confidence intervals Large sample inference Nonparametric estimation Point processes Poisson process Random fields Subsampling |
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