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Over/Under Dispersion Sometimes Necessitates Modifying Poisson Model with Illustration of Tetanus Cases and Deaths after Tsunami

Ramalingam Shanmugam


The outbreak of tetanus cases and deaths after a tsunami is quite common. The health professionals and governing agencies try to learn the pattern. Data analysts who are helping them are daunted with a phenomenon called over or under dispersion not to consider the Poisson model for the data. Model is an abstraction of the reality and is the foundation on which the trend of the incidences is to be understand. This article provides an approach, based on Neyman’s C ( ) principle, to decide whether the over or under dispersion is weak enough for the Poisson model to work for the data.


Count model, Neyman’s c (alpha) test, nuisance parameter.

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