Mixed Poisson distribution
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A mixed Poisson distribution is a univariate discrete probability distribution in stochastics. It results from assuming that the conditional distribution of a random variable, given the value of the rate parameter, is a Poisson distribution, and that the rate parameter itself is considered as a random variable. Hence it is a special case of a compound probability distribution. Mixed Poisson distributions can be found in actuarial mathematics as a general approach for the distribution of the number of claims and is also examined as an epidemiological model.[1] It should not be confused with compound Poisson distribution or compound Poisson process.[2]
Definition
Summarize
Perspective
A random variable X satisfies the mixed Poisson distribution with density π(λ) if it has the probability distribution[3]
If we denote the probabilities of the Poisson distribution by qλ(k), then
Properties
Summarize
Perspective
- The variance is always bigger than the expected value. This property is called overdispersion. This is in contrast to the Poisson distribution where mean and variance are the same.
- In practice, almost only densities of gamma distributions, logarithmic normal distributions and inverse Gaussian distributions are used as densities π(λ). If we choose the density of the gamma distribution, we get the negative binomial distribution, which explains why this is also called the Poisson gamma distribution.
In the following let be the expected value of the density and be the variance of the density.
Expected value
The expected value of the mixed Poisson distribution is
Variance
Skewness
The skewness can be represented as
Characteristic function
The characteristic function has the form
Where is the moment generating function of the density.
Probability generating function
For the probability generating function, one obtains[3]
Moment-generating function
The moment-generating function of the mixed Poisson distribution is
Examples
Summarize
Perspective
Theorem—Compounding a Poisson distribution with rate parameter distributed according to a gamma distribution yields a negative binomial distribution.[3]
Proof
Let be a density of a distributed random variable.
Therefore we get
Theorem—Compounding a Poisson distribution with rate parameter distributed according to an exponential distribution yields a geometric distribution.
Proof
Let be a density of a distributed random variable. Using integration by parts n times yields: Therefore we get
Table of mixed Poisson distributions
mixing distribution | mixed Poisson distribution[4] |
---|---|
Dirac | Poisson |
gamma, Erlang | negative binomial |
exponential | geometric |
inverse Gaussian | Sichel |
Poisson | Neyman |
generalized inverse Gaussian | Poisson-generalized inverse Gaussian |
generalized gamma | Poisson-generalized gamma |
generalized Pareto | Poisson-generalized Pareto |
inverse-gamma | Poisson-inverse gamma |
log-normal | Poisson-log-normal |
Lomax | Poisson–Lomax |
Pareto | Poisson–Pareto |
Pearson’s family of distributions | Poisson–Pearson family |
truncated normal | Poisson-truncated normal |
uniform | Poisson-uniform |
shifted gamma | Delaporte |
beta with specific parameter values | Yule |
References
Further reading
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