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Testing the equality of a large number of normal population means

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  • Park, Junyong
  • Park, DoHwan
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    Abstract

    It is challenging to consider the problem of testing the equality of normal population means when the number of populations is large compared to the sample sizes. In ANOVA with the assumption of homogeneous variance, the F-test is known as an exact test. When variances are heterogeneous, due to the complication, there are various tests with only approximate forms–either approximate chi-square or approximate F-test. Two types of tests are proposed with their asymptotic normality as the number of population increases. p-values from those tests are adjusted based on higher order asymptotics such as Edgeworth expansion so that the proposed tests can be considered even for moderate values of k. Numerical studies including simulations and real data examples are presented with comparison to existing tests.

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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 5 ()
    Pages: 1131-1149

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    Handle: RePEc:eee:csdana:v:56:y:2012:i:5:p:1131-1149

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Meta analysis; Testing the equality of means; Inhomogeneous variances; Asymptotic normality; Edgeworth expansion;

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    1. Tian, Lili, 2006. "Testing equality of inverse Gaussian means under heterogeneity, based on generalized test variable," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1156-1162, November.
    2. Joachim Hartung & Dogan Argaç & Kepher Makambi, 2002. "Small sample properties of tests on homogeneity in one—way Anova and Meta—analysis," Statistical Papers, Springer, vol. 43(2), pages 197-235, April.
    3. Saha, Krishna K. & Bilisoly, Roger, 2009. "Testing the homogeneity of the means of several groups of count data in the presence of unequal dispersions," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3305-3313, July.
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