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Testing normality in mixed models using a transformation method

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  • Wangli Xu
  • Yanwen Li
  • Dawo Song

Abstract

Statistical inference often assumes the normality of the variables involved in the model under study. The existing tests are for independent observations and may not be readily extended to handle the case with correlated ones. In this paper, a transformation method is recommended for normality checking in the two-way analysis of variance model. Its sampling properties are investigated. Simulation studies are carried out to examine the performance of the proposed methodology. Copyright Springer-Verlag 2013

Suggested Citation

  • Wangli Xu & Yanwen Li & Dawo Song, 2013. "Testing normality in mixed models using a transformation method," Statistical Papers, Springer, vol. 54(1), pages 71-84, February.
    • Handle: RePEc:spr:stpapr:v:54:y:2013:i:1:p:71-84
    DOI: 10.1007/s00362-011-0411-4
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    References listed on IDEAS

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    1. Srivastava, M. S., 1984. "A measure of skewness and kurtosis and a graphical method for assessing multivariate normality," Statistics & Probability Letters, Elsevier, vol. 2(5), pages 263-267, October.
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    3. Hwang, Yi-Ting & Wei, Peir Feng, 2006. "A novel method for testing normality in a mixed model of a nested classification," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1163-1183, November.
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    5. J. P. Royston, 1982. "The W Test for Normality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 176-180, June.
    6. Dieter Rasch & Thomas Rusch & Marie Šimečková & Klaus Kubinger & Karl Moder & Petr Šimeček, 2009. "Tests of additivity in mixed and fixed effect two-way ANOVA models with single sub-class numbers," Statistical Papers, Springer, vol. 50(4), pages 905-916, August.
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    Cited by:

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    2. Norbert Henze & Stefan Koch, 2020. "On a test of normality based on the empirical moment generating function," Statistical Papers, Springer, vol. 61(1), pages 17-29, February.

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