Infill asymptotics for adaptive kernel estimators of spatial intensity

Marie-Colette van Lieshout

doi:10.1111/anzs.12319

Infill asymptotics for adaptive kernel estimators of spatial intensity

Marie-Colette van Lieshout^*

^*Corresponding author for this work

Mathematics of Operations Research

Research output: Contribution to journal › Article › Academic › peer-review

2 Citations (Scopus)

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Abstract

We apply the Abramson principle to define adaptive kernel estimators for the intensity function of a spatial point process. We derive asymptotic expansions for the bias and variance under the regime that n independent copies of a simple point process in Euclidean space are superposed. The method is illustrated by means of a simple example and applied to tornado data.

Original language	English
Article number	12319
Pages (from-to)	159–181
Number of pages	23
Journal	Australian & New Zealand Journal of Statistics
Volume	63
Issue number	1
DOIs	https://doi.org/10.1111/anzs.12319
Publication status	Published - 1 Mar 2021

Keywords

UT-Hybrid-D
bandwidth
infill asymptotics
intensity function
mean squared error
point process
adaptive kernel estimator

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10.1111/anzs.12319Licence: CC BY-NC-ND

Van-lieshout-2021-Infill-asymptotics-for-adaptive-kerFinal published version, 297 KBLicence: CC BY-NC-ND

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KW - UT-Hybrid-D

KW - bandwidth

KW - infill asymptotics

KW - intensity function

KW - mean squared error

KW - point process

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Infill asymptotics for adaptive kernel estimators of spatial intensity

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Keywords

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