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Regression analysis of zero-inflated time-series counts: application to air pollution related emergency room visit data

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  • M. Tariqul Hasan
  • Gary Sneddon
  • Renjun Ma

Abstract

Time-series count data with excessive zeros frequently occur in environmental, medical and biological studies. These data have been traditionally handled by conditional and marginal modeling approaches separately in the literature. The conditional modeling approaches are computationally much simpler, whereas marginal modeling approaches can link the overall mean with covariates directly. In this paper, we propose new models that can have conditional and marginal modeling interpretations for zero-inflated time-series counts using compound Poisson distributed random effects. We also develop a computationally efficient estimation method for our models using a quasi-likelihood approach. The proposed method is illustrated with an application to air pollution-related emergency room visits. We also evaluate the performance of our method through simulation studies.

Suggested Citation

  • M. Tariqul Hasan & Gary Sneddon & Renjun Ma, 2012. "Regression analysis of zero-inflated time-series counts: application to air pollution related emergency room visit data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 467-476, June.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:467-476
    DOI: 10.1080/02664763.2011.595778
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    References listed on IDEAS

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    3. Renjun Ma & Bent Jørgensen, 2007. "Nested generalized linear mixed models: an orthodox best linear unbiased predictor approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 625-641, September.
    4. Y. Zhao & A. H. Lee & V. Burke & K. K. W. Yau, 2009. "Testing for zero-inflation in count series: application to occupational health," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(12), pages 1353-1359.
    5. Lee, Keunbaik & Joo, Yongsung & Song, Joon Jin & Harper, Dee Wood, 2011. "Analysis of zero-inflated clustered count data: A marginalized model approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 824-837, January.
    6. John M. Neuhaus & Charles E. McCulloch & Ross Boylan, 2011. "A Note on Type II Error Under Random Effects Misspecification in Generalized Linear Mixed Models," Biometrics, The International Biometric Society, vol. 67(2), pages 654-656, June.
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