Inference for Clustered Inhomogeneous Spatial Point Processes

We propose a method to test for significant differences in the levels of clustering between two spatial point processes (cases and controls) while taking into account differences in their first-order intensities. The key advance on earlier methods is that the controls are not assumed to be a Poisson...

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Bibliographic Details
Published in:Biometrics Vol. 65; no. 2; pp. 423 - 430
Main Authors: Henrys, P. A., Brown, P. E.
Format: Journal Article
Language:English
Published: Malden, USA Blackwell Publishing Inc 01.06.2009
Wiley-Blackwell Publishing
Blackwell Publishing Ltd
Subjects:
adults
Algorithms
Altitude
Biometric Methodology
Biometrics
biometry
Biometry - methods
Biostatistics
Cluster Analysis
Clustering
Computer Simulation
Data Interpretation, Statistical
diagnostic techniques
Ecology
Environmental epidemiology
Epidemiologic Research Design
Inference
Inhomogeneous
methods
Null hypothesis
P values
Pattern Recognition, Automated
Point estimators
Poisson process
Proportional Hazards Models
Reproducibility of Results
Risk Assessment
Risk Assessment - methods
Sampling distributions
Sensitivity and Specificity
Spatial point processes
Spatial points
Statistical methods
Trees
tropical forests
ISSN:0006-341X, 1541-0420, 1541-0420
Online Access:Get full text
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Summary:We propose a method to test for significant differences in the levels of clustering between two spatial point processes (cases and controls) while taking into account differences in their first-order intensities. The key advance on earlier methods is that the controls are not assumed to be a Poisson process. Inference and diagnostics are based around the inhomogeneous K-function with confidence envelopes obtained from either resampling events in a nonparametric bootstrap approach, or simulating new events as in a parametric bootstrap. Methods developed are demonstrated using the locations of adult and juvenile trees in a tropical forest. A simulation study briefly examines the accuracy and power of the inferential procedures.
Bibliography:http://dx.doi.org/10.1111/j.1541-0420.2008.01070.x
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ArticleID:BIOM1070
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/j.1541-0420.2008.01070.x