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February 1997 Kaplan-Meier estimators of distance distributions for spatial point processes
Adrian Baddeley, Richard D. Gill
Ann. Statist. 25(1): 263-292 (February 1997). DOI: 10.1214/aos/1034276629

Abstract

When a spatial point process is observed through a bounded window, edge effects hamper the estimation of characteristics such as the empty space function F, the nearest neighbor distance distribution G and the reduced second-order moment function K. Here we propose and study product-limit type estimators of F, G and K based on the analogy with censored survival data: the distance from a fixed point to the nearest point of the process is right-censored by its distance to the boundary of the window. The resulting estimators have a ratio-unbiasedness property that is standard in spatial statistics. We show that the empty space function F of any stationary point process is absolutely continuous, and so is the product-limit estimator of F. The estimators are strongly consistent when there are independent replications or when the sampling window becomes large. We sketch a CLT for independent replications within a fixed observation window and asymptotic theory for independent replications of sparse Poisson processes. In simulations the new estimators are generally more efficient than the "border method" estimator but (for estimators of K), somewhat less efficient than sophisticated edge corrections.

Citation

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Adrian Baddeley. Richard D. Gill. "Kaplan-Meier estimators of distance distributions for spatial point processes." Ann. Statist. 25 (1) 263 - 292, February 1997. https://doi.org/10.1214/aos/1034276629

Information

Published: February 1997
First available in Project Euclid: 10 October 2002

zbMATH: 0870.62028
MathSciNet: MR1429925
Digital Object Identifier: 10.1214/aos/1034276629

Subjects:
Primary: 60D05, 62G05, 62H11

Rights: Copyright © 1997 Institute of Mathematical Statistics

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Vol.25 • No. 1 • February 1997
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