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Generalized p value tests for variance components in a class of linear mixed models

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Listed:
  • Liwen Xu

    (North China University of Technology)

  • Hongxia Guo

    (North China University of Technology)

  • Shenghua Yu

    (Hunan University)

Abstract

The problems of testing hypotheses on variance components in linear mixed effects models have been addressed by various workers, although existing methodology is still restricted to a narrow range of models. To overcome this difficulty we develop new general p value tests in general settings. The p values are motivated by a useful matrix inequality. It is shown that the proposed test is invariant under the group of location-scale transformations. Numerical results show that the test can control the Type I errors satisfactorily, and it also exhibits good power properties. Most importantly, the new methods are simple and easy to apply.

Suggested Citation

  • Liwen Xu & Hongxia Guo & Shenghua Yu, 2018. "Generalized p value tests for variance components in a class of linear mixed models," Statistical Papers, Springer, vol. 59(2), pages 581-604, June.
    • Handle: RePEc:spr:stpapr:v:59:y:2018:i:2:d:10.1007_s00362-016-0778-3
    DOI: 10.1007/s00362-016-0778-3
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    References listed on IDEAS

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    1. Ye, Ren-Dao & Ma, Tie-Feng & Wang, Song-Gui, 2010. "Inferences on the common mean of several inverse Gaussian populations," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 906-915, April.
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    3. Weerahandi, Samaradasa, 1987. "Testing Regression Equality with Unequal Variances," Econometrica, Econometric Society, vol. 55(5), pages 1211-1215, September.
    4. Ciprian M. Crainiceanu & David Ruppert, 2004. "Likelihood ratio tests in linear mixed models with one variance component," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 165-185, February.
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