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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. 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.
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    4. 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.
    5. Norbert Henze, 2002. "Invariant tests for multivariate normality: a critical review," Statistical Papers, Springer, vol. 43(4), pages 467-506, October.
    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:

    1. Paulo Rodrigues & Elsa Moreira & Vera Jesus & João Mexia, 2014. "Structured orthogonal families of one and two strata prime basis factorial models," Statistical Papers, Springer, vol. 55(3), pages 603-614, August.
    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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