2009-06-15, English, Article, Journal or magazine article edition: Overall and pairwise segregation tests based on nearest neighbor contingency tables. Ceyhan, Elvan

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Physical Description
  • Magazine/​Journal article
Published
  • Elsevier B.V., 2009 June 15
Language
  • English

Edition details

Title
  • Overall and pairwise segregation tests based on nearest neighbor contingency tables.
Appears In
  • Computational Statistics and Data Analysis, v.53, no.8, 2009 June 15, p.2786(23) (ISSN: 0167-9473)
Author
  • Ceyhan, Elvan
Published
  • Elsevier B.V., 2009 June 15
Physical Description
  • Magazine/​Journal article
Subjects
Target Audience
  • Academic
Summary
  • To link to full-text access for this article, visit this link: http:/​/​dx.doi.org/​10.1016/​j.csda.2008.08.002 Byline: Elvan Ceyhan Abstract: Multivariate 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. Author Affiliation: Department of Mathematics, Koc University, 34450 Sariyer, Istanbul, Turkey
Terms of Use
  • COPYRIGHT 2009 Elsevier B.V.
Language
  • English
Database
  • Gale Academic OneFile
Identifier
  • 350509740

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