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Bootstrap goodness-of-fit test for the beta-binomial model

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  • Steven Garren
  • Richard Smith
  • Walter Piegorsch

Abstract

A common question in the analysis of binary data is how to deal with overdispersion. One widely advocated sampling distribution for overdispersed binary data is the beta-binomial model. For example, this distribution is often used to model litter effects in toxicological experiments. Testing the null hypothesis of a beta-binomial distribution against all other distributions is difficult, however, when the litter sizes vary greatly. Herein, we propose a test statistic based on combining Pearson statistics from individual litter sizes, and estimate the p-value using bootstrap techniques. A Monte Carlo study confirms the accuracy and power of the test against a beta-binomial distribution contaminated with a few outliers. The method is applied to data from environmental toxicity studies.

Suggested Citation

  • Steven Garren & Richard Smith & Walter Piegorsch, 2001. "Bootstrap goodness-of-fit test for the beta-binomial model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(5), pages 561-571.
    • Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:561-571
    DOI: 10.1080/02664760120047898
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    References listed on IDEAS

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    1. Steven T. Garren & Richard L. Smith & Walter W. Piegorsch, 2000. "On a Likelihood-Based Goodness-of-Fit Test of the Beta-Binomial Model," Biometrics, The International Biometric Society, vol. 56(3), pages 947-949, September.
    2. Paul Zhang, 1999. "Omnibus test of normality using the Q statistic," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(4), pages 519-528.
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    Cited by:

    1. Kolossiatis, M. & Griffin, J.E. & Steel, M.F.J., 2011. "Modeling overdispersion with the normalized tempered stable distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2288-2301, July.

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