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A new discrete distribution: properties and applications in medical care

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  • Emilio Gómez Déniz

Abstract

This paper proposes a simple and flexible count data regression model which is able to incorporate overdispersion (the variance is greater than the mean) and which can be considered a competitor to the Poisson model. As is well known, this classical model imposes the restriction that the conditional mean of each count variable must equal the conditional variance. Nevertheless, for the common case of well-dispersed counts the Poisson regression may not be appropriate, while the count regression model proposed here is potentially useful. We consider an application to model counts of medical care utilization by the elderly in the USA using a well-known data set from the National Medical Expenditure Survey (1987), where the dependent variable is the number of stays after hospital admission, and where 10 explanatory variables are analysed.

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  • Emilio Gómez Déniz, 2013. "A new discrete distribution: properties and applications in medical care," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2760-2770, December.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2760-2770
    DOI: 10.1080/02664763.2013.827161
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    4. Zhao, Xiaobing & Zhou, Xian, 2012. "Estimation of medical costs by copula models with dynamic change of health status," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 480-491.
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