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A Score Test for Testing a Marginalized Zero-Inflated Poisson Regression Model Against a Marginalized Zero-Inflated Negative Binomial Regression Model

Author

Listed:
  • Gul Inan

    (Middle East Technical University)

  • John Preisser

    (University of North Carolina)

  • Kalyan Das

    (University of Calcutta)

Abstract

Marginalized zero-inflated count regression models (Long et al. in Stat Med 33(29):5151–5165, 2014) provide direct inference on overall exposure effects. Unlike standard zero-inflated models, marginalized models specify a regression model component for the marginal mean in addition to a component for the probability of an excess zero. This study proposes a score test for testing a marginalized zero-inflated Poisson model against a marginalized zero-inflated negative binomial model for model selection based on an assessment of over-dispersion. The sampling distribution and empirical power of the proposed score test are investigated via a Monte Carlo simulation study, and the procedure is illustrated with data from a horticultural experiment. Supplementary materials accompanying this paper appear on-line.

Suggested Citation

  • Gul Inan & John Preisser & Kalyan Das, 2018. "A Score Test for Testing a Marginalized Zero-Inflated Poisson Regression Model Against a Marginalized Zero-Inflated Negative Binomial Regression Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 113-128, March.
    • Handle: RePEc:spr:jagbes:v:23:y:2018:i:1:d:10.1007_s13253-017-0314-5
    DOI: 10.1007/s13253-017-0314-5
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    References listed on IDEAS

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    1. David Todem & KyungMann Kim & Wei-Wen Hsu, 2016. "Marginal mean models for zero-inflated count data," Biometrics, The International Biometric Society, vol. 72(3), pages 986-994, September.
    2. Lim, Hwa Kyung & Song, Juwon & Jung, Byoung Cheol, 2013. "Score tests for zero-inflation and overdispersion in two-level count data," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 67-82.
    3. Habtamu K. Benecha & Brian Neelon & Kimon Divaris & John S. Preisser, 2017. "Marginalized mixture models for count data from multiple source populations," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-17, December.
    4. Martin Ridout & John Hinde & Clarice G. B. Demétrio, 2001. "A Score Test for Testing a Zero‐Inflated Poisson Regression Model Against Zero‐Inflated Negative Binomial Alternatives," Biometrics, The International Biometric Society, vol. 57(1), pages 219-223, March.
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    6. Yang, Zhao & Hardin, James W. & Addy, Cheryl L., 2010. "Score tests for overdispersion in zero-inflated Poisson mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1234-1246, May.
    7. Hossein Zamani & Noriszura Ismail, 2013. "Score test for testing zero-inflated Poisson regression against zero-inflated generalized Poisson alternatives," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 2056-2068, September.
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