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A Monte Carlo permutation procedure for testing variance components in generalized linear regression models

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  • Yahia S. El-Horbaty

    (Helwan University)

Abstract

Testing zero variance components is of utmost importance in various applications empowered by the use of mixed-effects models. Focusing on generalized linear models, this article proposes a permutation test using an analogue of the ANOVA test statistic that merely requires fitting the null model with independent observations. Monte Carlo simulations reveal that the new test has correct Type-I error rate and that its power compares favorably to an existing bootstrap score test. A real data application illustrates the advantageous capability of the proposed test in detecting the need for random effects.

Suggested Citation

  • Yahia S. El-Horbaty, 2024. "A Monte Carlo permutation procedure for testing variance components in generalized linear regression models," Computational Statistics, Springer, vol. 39(5), pages 2605-2621, July.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:5:d:10.1007_s00180-023-01403-y
    DOI: 10.1007/s00180-023-01403-y
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    References listed on IDEAS

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    1. Yoonsang Kim & Young-Ku Choi & Sherry Emery, 2013. "Logistic Regression With Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages," The American Statistician, Taylor & Francis Journals, vol. 67(3), pages 171-182, August.
    2. Juvêncio Nobre & Julio Singer & Pranab Sen, 2013. "U-tests for variance components in linear mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 580-605, November.
    3. Oliver E. Lee & Thomas M. Braun, 2012. "Permutation Tests for Random Effects in Linear Mixed Models," Biometrics, The International Biometric Society, vol. 68(2), pages 486-493, June.
    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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