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Comparison of some homogeneity tests in analysis of over-dispersed binomial data

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Abstract

This paper compares the procedures based on the extended quasi-likelihood, pseudo-likelihood and quasi-likelihood approaches for testing homogeneity of several proportions for over-dispersed binomial data. The type I error of the Wald tests using the model-based and robust variance estimates, the score test, and the extended quasi-likelihood ratio test (deviance reduction test) were examined by simulation. The extended quasi-likelihood method performs less well when mean responses are close to 1 or 0. The model-based Wald test based on the quasi-likelihood performs the best in maintaining the nominal level. The score test performs less well when the intracluster correlations are large or heterogeneous. In summary: (i) both the quasilikelihood and pseudo-likelihood methods appear to be acceptable but care must be taken when overfitting a variance function with small sample sizes; (ii) the extended quasi-likelihood approach is the least favourable method because its nominal level is much too high; and (iii) the robust variance estimator performs poorly, particularly when the sample size is small.

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References

  • Alm, H. and Chen, J.J. (1995) Generation of over-dispersed and under-dispersed binomial variates. Journal of Computational and Graphical Statistics 4, 35–64.

    Google Scholar 

  • Carroll, R.J. and Ruppert, D. (1982) Robust estimation in heteroscedastic linear models. Annals of Statistics 10, 429–41.

    Google Scholar 

  • Chen, J.J. and Li, L. (1994) Dose-response modeling of trinomial responses from developmental experiments. Statistica Sinica 4, 265–74.

    Google Scholar 

  • Clark, S.J. and Perry, J.N. (1989) Estimation of the negative binomial parameter by maximum quasilikelihood. Biometrics 45, 309–16.

    Google Scholar 

  • Crowder, M.J. (1978) Beta-binomial ANOVA for proportions. Applied Statistics 27, 34–7.

    Google Scholar 

  • Davidian, M. and Carroll, R.J. (1987) Variance function estimation. Journal of the American Statistical Association 82, 1079–91.

    Google Scholar 

  • Drum, M. and McCullagh, P. (1993) Comment on regression models for discrete longitudinal response. Statistical Science 8, 300–1.

    Google Scholar 

  • Haseman, J.K. and Hogan, M.D. (1975) Selection of experimental unit in the teratology studies. Teratology 12, 165–72.

    Google Scholar 

  • Kupper, L. L., Portier, C., Hogan, M.D. and Yamamoto, E. (1986) The impact of litter effects on dose-response modeling in teratology. Biometrics 42, 85–98.

    Google Scholar 

  • Liang, K.Y. and Hanfalt, S.L. (1994) On the use of the quasi-likelihood method in teratology experiments. Biometrics 50, 872–80.

    Google Scholar 

  • Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika 73, 13–22.

    Google Scholar 

  • McCullagh, P. and Nelder, J.A. (1989) Generalized Linear Models, 2nd edn. Chapman & Hall, New York.

    Google Scholar 

  • Moore, D.F. and Tsiatis, A. (1991) Robust estimation of variance in moment methods for extra-binomial and extra-Poisson variation. Biometrics 47, 383–401.

    Google Scholar 

  • Nelder, J.A. and Pregibon, D. (1987) An extended quasi-likelihood function. Biometrika 74, 221–32.

    Google Scholar 

  • Paul, S.R. (1982) Analysis of proportions of affected foetuses in teratological experiments. Biometrics 38, 361–70.

    Google Scholar 

  • Paul, S.R. and Islam, A.S. (1993) Analysis of proportions based on parametric and semi-parametric models. Unpublished.

  • Smyth, G.K. (1989) Generalized linear models with varying dispersion. Journal of the Royal Statistical Society B 51, 47–60.

    Google Scholar 

  • Williams, D.A. (1982) Extra-binomial variation in logistic linear models. Applied Statistics 31, 144–8.

    Google Scholar 

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Chen, J.J., Ahn, H. & Cheng, K.F. Comparison of some homogeneity tests in analysis of over-dispersed binomial data. Environ Ecol Stat 1, 315–324 (1994). https://doi.org/10.1007/BF00469428

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  • DOI: https://doi.org/10.1007/BF00469428

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