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A contingency table approach based on nearest neighbour relations for testing self and mixed correspondence
Ceyhan, Elvan (North Carolina State University. Department of Statistics)

Date: 2018
Abstract: Nearest neighbour methods are employed for drawing inferences about spatial patterns of points from two or more classes. We introduce a new pattern called correspondence which is motivated by (spatial) niche/habitat specificity and segregation, and define an associated contingency table called a correspondence contingency table, and examine the relation of correspondence with the motivating patterns (namely, segregation and niche specificity). We propose tests based on the correspondence contingency table for testing self and mixed correspondence and determine the appropriate null hypotheses and the underlying conditions appropriate for these tests. We compare finite sample performance of the tests in terms of empirical size and power by extensive Monte Carlo simulations and illustrate the methods on two artificial data sets and one real-life ecological data set.
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Language: Anglès
Document: article ; recerca ; publishedVersion
Subject: Association ; Complete spatial randomness ; Habitat/niche specificity ; Independence ; Random labelling ; Segregation
Published in: SORT : statistics and operations research transactions, Vol. 42 Núm. 2 (July-December 2018) , p. 125-158 (Articles) , ISSN 2013-8830

Adreça original: https://www.raco.cat/index.php/SORT/article/view/347336
Adreça alternativa: https://www.raco.cat/index.php/SORT/article/view/347336/438495
DOI: 10.2436/20.8080.02.72

34 p, 520.2 KB

The record appears in these collections:
Articles > Published articles > SORT
Articles > Research articles

 Record created 2019-01-08, last modified 2020-08-30

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