A full likelihood procedure of exchangeable negative binomials for modelling correlated and overdispersed count data |
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Authors: | Fei Tan Gibson Johnston Rayner Xiaodong Wang Hanxiang Peng |
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Institution: | 1. Institute of Public Health, College of Pharmacy and Pharmaceutical Sciences, Florida A&M University, Tallahassee, FL 32307, USA;2. Department of Mathematics, University of Mississippi, University, MS 38677-1848, USA;3. Sanofi-Aventis, Bridgewater, NJ 08807, USA;4. Department of Mathematical Sciences, Indiana University–Purdue University At Indianapolis, Indianapolis, IN 46202-3216, USA |
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Abstract: | This paper introduces an exchangeable negative binomial distribution resulting from relaxing the independence of the Bernoulli sequence associated with a negative binomial distribution to exchangeability. It is demonstrated that the introduced distribution is a mixture of negative binomial distributions and can be characterized by infinitely many parameters that form a completely monotone sequence. The moments of the distribution are derived and a small simulation is conducted to illustrate the distribution. For data analytic purposes, two methods, truncation and completely-monotone links, are given for converting the saturated distribution of infinitely many parameters to parsimonious distributions of finitely many parameters. A full likelihood procedure is described which can be used to investigate correlated and overdispersed count data common in biomedical sciences and teratology. In the end, the introduced distribution is applied to analyze a real clinical data of burn wounds on patients. |
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Keywords: | Beta-binomial Complete monotonicity Exchangeability Negative binomial Overdispersion |
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