Bootstrapping an inhomogeneous point process

Ji Meng Loh

doi:10.1016/j.jspi.2009.09.003

Bootstrapping an inhomogeneous point process

Ji Meng Loh

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

10 Scopus citations

Abstract

In this paper, we focus on resampling non-stationary weakly dependent point processes in two dimensions to make inference on the inhomogeneous K function (Baddeley et al., 2000). We provide theoretical results that show a consistency result of the bootstrap estimates of the variance as the observation region and resampling blocks increase in size. We present results of a simulation study that examines the performance of nominal 95% confidence intervals for the inhomogeneous K function obtained via our bootstrap procedure. The procedure is also applied to a rainforest dataset.

Original language	English (US)
Pages (from-to)	734-749
Number of pages	16
Journal	Journal of Statistical Planning and Inference
Volume	140
Issue number	3
DOIs	https://doi.org/10.1016/j.jspi.2009.09.003
State	Published - Mar 2010
Externally published	Yes

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

Keywords

Inhomogeneous K function
Inhomogeneous point process
Marked point bootstrap
Spatial bootstrap

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10.1016/j.jspi.2009.09.003

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title = "Bootstrapping an inhomogeneous point process",

abstract = "In this paper, we focus on resampling non-stationary weakly dependent point processes in two dimensions to make inference on the inhomogeneous K function (Baddeley et al., 2000). We provide theoretical results that show a consistency result of the bootstrap estimates of the variance as the observation region and resampling blocks increase in size. We present results of a simulation study that examines the performance of nominal 95% confidence intervals for the inhomogeneous K function obtained via our bootstrap procedure. The procedure is also applied to a rainforest dataset.",

keywords = "Inhomogeneous K function, Inhomogeneous point process, Marked point bootstrap, Spatial bootstrap",

author = "Loh, {Ji Meng}",

note = "Funding Information: This work is supported in part by NSF award AST-0507687. The author would also like to thank the anonymous referees for their helpful comments. ",

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N1 - Funding Information: This work is supported in part by NSF award AST-0507687. The author would also like to thank the anonymous referees for their helpful comments.

PY - 2010/3

Y1 - 2010/3

N2 - In this paper, we focus on resampling non-stationary weakly dependent point processes in two dimensions to make inference on the inhomogeneous K function (Baddeley et al., 2000). We provide theoretical results that show a consistency result of the bootstrap estimates of the variance as the observation region and resampling blocks increase in size. We present results of a simulation study that examines the performance of nominal 95% confidence intervals for the inhomogeneous K function obtained via our bootstrap procedure. The procedure is also applied to a rainforest dataset.

AB - In this paper, we focus on resampling non-stationary weakly dependent point processes in two dimensions to make inference on the inhomogeneous K function (Baddeley et al., 2000). We provide theoretical results that show a consistency result of the bootstrap estimates of the variance as the observation region and resampling blocks increase in size. We present results of a simulation study that examines the performance of nominal 95% confidence intervals for the inhomogeneous K function obtained via our bootstrap procedure. The procedure is also applied to a rainforest dataset.

KW - Inhomogeneous K function

KW - Inhomogeneous point process

KW - Marked point bootstrap

KW - Spatial bootstrap

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VL - 140

SP - 734

EP - 749

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

IS - 3

ER -

Bootstrapping an inhomogeneous point process

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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