Document Type

Journal Article

Department/Unit

Department of Mathematics

Language

English

Abstract

For a spatial point pattern observed in a bounded window, we propose using discrepancies, which are measures of uniformity in the quasi-Monte Carlo method, to test the complete spatial randomness hypothesis. Tests using these discrepancies are in fact goodness-of-fit tests for uniform distribution. The discrepancies are free from edge effects and, unlike the popular maximum absolute pointwise difference statistic of a summary function over a suitably chosen range, do not have an arbitrary parameter. Simulation studies show that they are often more powerful when a given pattern is a realization of a process with long-range interaction or a nonstationary process. © 2007 American Statistical Association.

Keywords

Complete spatial randomness, Discrepancy, Quasi-Monte Carlo method, Spatial point process

Publication Date

5-31-2007

Source Publication Title

Journal of Computational and Graphical Statistics

Volume

16

Issue

2

Start Page

378

End Page

398

Publisher

Taylor & Francis

Peer Reviewed

1

Copyright

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Computational and Graphical Statistics on 5-31-2007, available online: http://www.tandfonline.com/10.1198/106186007X208966.

Funder

This research was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Numbers HKBU2048/02P and HKBU200503) and an FRG grant from the Hong Kong Baptist University.

DOI

10.1198/106186007X208966

Link to Publisher's Edition

http://dx.doi.org/10.1198/106186007X208966

ISSN (print)

10618600

ISSN (electronic)

15372715

Included in

Mathematics Commons

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