Gamma Poisson Mixture with finite Gamma

In summary, the speaker is working with a Gamma-Poisson mixture distribution with a finite support. They are seeking help in deriving the mean and Likelihood function due to the complicated expressions involving the incomplete gamma function. They are wondering if there are other models for the Poisson frequency that may be easier to work with. They also mention the potential need for numerical methods for integration and/or optimization.
  • #1
ARDE
5
0
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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  • #2
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.
 

What is a Gamma Poisson Mixture with finite Gamma?

A Gamma Poisson Mixture with finite Gamma is a statistical model used to analyze count data. It assumes that the data is a mixture of two distributions - a Poisson distribution and a Gamma distribution. The Gamma distribution is used to account for overdispersion in the data.

How is a Gamma Poisson Mixture with finite Gamma different from a regular Gamma Poisson Mixture?

A regular Gamma Poisson Mixture assumes that the Gamma distribution is infinite, while a Gamma Poisson Mixture with finite Gamma assumes that the Gamma distribution is finite. This means that the model takes into account potential upper limits on the data, which can improve its accuracy.

When should a Gamma Poisson Mixture with finite Gamma be used?

This model is useful when analyzing count data with potential upper limits, such as the number of customers in a store or the number of defects in a product. It can also be used when the data shows overdispersion, meaning that the variance is larger than the mean.

What are the advantages of using a Gamma Poisson Mixture with finite Gamma?

By accounting for overdispersion and potential upper limits, this model can provide more accurate and reliable results compared to other models. It also allows for more flexibility in analyzing count data, as it can handle a wider range of data distributions.

Are there any limitations to using a Gamma Poisson Mixture with finite Gamma?

One limitation is that the model requires a larger sample size to accurately estimate the parameters. It also assumes that the data follows a mixture of two specific distributions, which may not always be the case. Additionally, the interpretation of the results may be more complex compared to other models.

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