Publication:
Overall and pairwise segregation tests based on nearest neighbor contingency tables

dc.contributor.departmentDepartment of Mathematics
dc.contributor.kuauthorCeyhan, Elvan
dc.contributor.schoolcollegeinstituteCollege of Sciences
dc.date.accessioned2024-11-09T23:52:28Z
dc.date.issued2009
dc.description.abstractMultivariate interaction between two or more classes (or species) has important consequences in many fields and may cause multivariate clustering patterns such as spatial segregation or association. The spatial segregation occurs when members of a class tend to be found near members of the same class (i.e., near conspecifics) while spatial association occurs when members of a class tend to be found near members of the other class or classes. These patterns can be studied using a nearest neighbor contingency table (NNCT). The null hypothesis is randomness in the nearest neighbor (NN) structure, which may result from - among other patterns - random labeling (RL) or complete spatial randomness (CSR) of points from two or more classes (which is called the CSR independence, henceforth). New versions of overall and cell-specific tests based on NNCTs (i.e., NNCT-tests) are introduced and compared with Dixon's overall and cell-specific tests and various other spatial clustering methods. Overall segregation tests are used to detect any deviation from the null case, while the cell-specific tests are post hoc pairwise spatial interaction tests that are applied when the overall test yields a significant result. The distributional properties of these tests are analyzed and finite sample performance of the tests are assessed by an extensive Monte Carlo simulation study. Furthermore, it is shown that the new NNCT-tests have better performance in terms of Type I error and power estimates. The methods are also applied on two real life data sets for illustrative purposes. (c) 2008 Elsevier B.V. All rights reserved.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.issue8
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.volume53
dc.identifier.doi10.1016/j.csda.2008.08.002
dc.identifier.eissn1872-7352
dc.identifier.issn0167-9473
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-62849099012
dc.identifier.urihttps://doi.org/10.1016/j.csda.2008.08.002
dc.identifier.urihttps://hdl.handle.net/20.500.14288/14858
dc.identifier.wos265571000002
dc.keywordsSpatial segregation
dc.keywordsPattern-analysis
dc.keywordsK-function
dc.keywordsTree
dc.keywordsDispersion
dc.keywordsInference
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofComputational Statistics and Data Analysis
dc.subjectComputer science
dc.subjectStatistics
dc.subjectProbability
dc.titleOverall and pairwise segregation tests based on nearest neighbor contingency tables
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorCeyhan, Elvan
local.publication.orgunit1College of Sciences
local.publication.orgunit2Department of Mathematics
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relation.isOrgUnitOfPublication.latestForDiscovery2159b841-6c2d-4f54-b1d4-b6ba86edfdbe
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