 K-sample tests for equality of variances of random fuzzy setsAna Belén Ramos-Guajardo and María Asunción Lubiano Computational Statistics & Data Analysis, 2012, vol. 56, issue 4, 956-966 Abstract: The problem of testing equality of variances often arises when distributions of random variables are compared or linear models between them are considered. The usual tests for variances given normality of the underlying populations are highly non-robust to non-normality and are strongly dependent on the kurtosis. Some alternative formulations of Levene’s test statistic for testing the homoscedasticity have been shown to be powerful and robust under non-normality. On the basis of Levene’s classical procedure, a test for the equality of variances of k fuzzy-valued random elements is developed. Accordingly, consistent asymptotic and bootstrap tests are established and their empirical behaviour is analyzed by means of extensive simulation studies. In addition, the proposed test is compared with a Bartlett-type test. A case-study illustrating the applicability of the procedure is presented. Keywords: Equality of variances; Levene’s test; Random fuzzy set; Hypothesis testing; Dθφ metric (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:4:p:956-966 DOI: 10.1016/j.csda.2010.11.025 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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