Gamma Poisson Mixture with finite Gamma

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SUMMARY

The discussion centers on deriving the mean and likelihood function for a finite support Gamma-Poisson mixture distribution, specifically within the interval [0,1]. The user, Arde, encounters challenges due to the presence of an incomplete gamma function, which complicates the integration process and prevents a closed-form solution. Participants suggest exploring alternative models for the Poisson frequency, such as uniform, triangular, or beta distributions, which may simplify calculations. Numerical methods for integration and optimization are recommended to address the instability caused by complicated normalization constants in truncated distributions.

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  • Understanding of Gamma-Poisson mixture distributions
  • Familiarity with incomplete gamma functions
  • Knowledge of numerical methods for integration and optimization
  • Experience with alternative probability distributions (uniform, triangular, beta)
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Statisticians, data scientists, and researchers working with mixture distributions, particularly those dealing with finite support scenarios and numerical methods for statistical analysis.

ARDE
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Dear all

I am working with a Gamma-Poisson mixture distribution where (and this is not usual) the support of the Gamma distribution (in fact, I am able to restrict to the neg exponential instead of the Gamma...) is finite, e.g. [0,1].
I would like to derive the mean and a Likelihood function.

Normally, you end up with a negative binomial distribution for the above mixture, i.e. mean and Likelihood are straightforward.
But due to the finite support I end up with an incomplete gamma function in my expressions and I am not able to solve the integral "nicely" and give a closed expression for the mean.

My question: Do you have any experience with such a right truncated gamma poisson mixture? Or any hints where I could find some similar computations that could be helpful?

Many thanks in advance,
regards

Arde
 
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It begs the question, do you *have* to use a truncated distribution? Other models for the Poisson frequency such as uniform, triangular or beta might be easier to work with.

Either way you may need numerical methods for integration and/or optimisation, which itself is not a problem, but the complicated normalisation constants in truncated distributions tend to make the calculations all the more unstable.
 

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