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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/113026

    Title: A Revisit to Test the Equality of Variances of Several Populations
    Other Titles: 英文
    Authors: Chang Ching-Hui;Nabendu Pal;Lin Jyh-Jiuan,
    Keywords: F-distribution chi-square distribution;Power;Size;Unbalanced design;Variance
    Date: 2017-11-19
    Issue Date: 2018-03-29 12:11:15 (UTC+8)
    Abstract: We revisit the problem of testing homoscedasticity (or, equality of variances) of several normal populations which has applications in many statistical analyses, including design of experiments. The standard text books and widely used statistical packages propose a few popular tests including Bartlett's test, Levene's test and a few adjustments of the latter. Apparently, the popularity of these tests have been based on limited simulation study carried out a few decades ago. The traditional tests, including the classical likelihood ratio test (LRT), are asymptotic in nature, and hence do not perform well for small sample sizes. In this paper we propose a simple parametric bootstrap (PB) modification of the LRT, and compare it against the other popular tests as well as their PB versions in terms of size and power. Our comprehensive simulation study bursts some popularly held myths about the commonly used tests and sheds some new light on this important problem. Though most popular statistical software/packages suggest using Bartlette's test, Levene's test, or modified Levene's test among a few others, our extensive simulation study, carried out under both the normal model as well as several non-normal models clearly shows that a PB version of the modified Levene's test (which does not use the F-distribution cut-off point as its critical value), and Loh's exact test are the “best” performers in terms of overall size as well as power.
    Relation: Communications in Statistics - Simulation and Computation 46(8), p.6360-6384
    DOI: 10.1080/03610918.2016.1202277
    Appears in Collections:[Graduate Institute & Department of Statistics] Journal Article

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