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Modeling of paired zero-inflated continuous data without breaking down paired designs

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  • Tiago R. Pellegrini
  • M. Tariqul Hasan
  • Renjun Ma

Abstract

Paired data have been widely collected in the efficiency studies of a new method against an established method in environmental, ecological and medical studies. For example, in comparative fishing studies, ability of catching target species (fish catch) or reducing the catch of non-target species (fish bycatch) is usually investigated through a paired design. These paired fish catches by weight are generally skewed and continuous, but with a significant portion of exact zeros (no catch). Such zero-inflated continuous data are traditionally handled by two-part models where the zero and positive components are handled separately; however, this separation generally destroys paired structure, and thus may result in substantial difficulty in characterizing the relative efficiency between two methods. To overcome this problem, we consider compound Poisson mixed model for paired data with which the zero and non-zero components are characterized in an integral way. In our approach, the clustering effects by pair are captured by incorporating relevant random effects. Our model is estimated using orthodox best linear unbiased predictor approach. Unlike two-part models, our approach unifies inferences of zero and positive components. Our method is illustrated with analyses of winter flounder bycatch data and ultrasound safety data.

Suggested Citation

  • Tiago R. Pellegrini & M. Tariqul Hasan & Renjun Ma, 2017. "Modeling of paired zero-inflated continuous data without breaking down paired designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2427-2443, October.
  • Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2427-2443
    DOI: 10.1080/02664763.2016.1254734
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    References listed on IDEAS

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    1. Renjun Ma, 2003. "Random effects Cox models: A Poisson modelling approach," Biometrika, Biometrika Trust, vol. 90(1), pages 157-169, March.
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    4. 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.
    5. Il Do Ha & Youngjo Lee, 2005. "Comparison of hierarchical likelihood versus orthodox best linear unbiased predictor approaches for frailty models," Biometrika, Biometrika Trust, vol. 92(3), pages 717-723, September.
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