Generalized Poisson Distribution

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Description

The density function is provided for the univariate, discrete, generalized Poisson distribution with location parameter lambda and scale parameter omega.

Usage

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dgpois(x, lambda=0, omega=0, log=FALSE)

Arguments

x

This is a vector of quantiles.

lambda

This is the parameter lambda.

omega

This is the parameter omega, which should be in the interval [0,1) for positive counts.

log

Logical. If log=TRUE, then the logarithm of the density is returned.

Details

  • Application: Discrete Univariate

  • Density: (1 - omega) lambda (((1 - omega) lambda + omega theta)^(y-1) / theta!) e(-((1 - omega) lambda + omega theta))

  • Inventor: Consul (1989) and Ntzoufras et al. (2005)

  • Notation 1: theta ~ GP(lambda, omega)

  • Notation 2: p(theta) = GP(theta | lambda, omega)

  • Parameter 1: location parameter lambda

  • Parameter 2: scale parameter omega in [0,1)

  • Mean: E(theta) = lambda

  • Variance: var(theta) = lambda(1 - omega)^(-2)

The generalized Poisson distribution (Consul, 1989) is also called the Lagrangian Poisson distribution. The simple Poisson distribution is a special case of the generalized Poisson distribution. The generalized Poisson distribution is used in generalized Poisson regression as an extension of Poisson regression that accounts for overdispersion.

The dgpois function is parameterized according to Ntzoufras et al. (2005), which is easier to interpret and estimates better with MCMC.

Valid values for omega are in the interval [0,1) for positive counts. For omega = 0, the generalized Poisson reduces to a simple Poisson with mean lambda. Note that it is possible for omega < 0, but this implies underdispersion in count data, which is uncommon. The dgpois function returns warnings or errors, so omega should be non-negative here.

The dispersion index (DI) is a variance-to-mean ratio, and is DI = (1 - omega)^(-2). A simple Poisson has DI=1. When DI is far from one, the assumption that the variance equals the mean of a simple Poisson is violated.

Value

dgpois gives the density.

References

Consul, P. (1989). '"Generalized Poisson Distribution: Properties and Applications". Marcel Decker: New York, NY.

Ntzoufras, I., Katsis, A., and Karlis, D. (2005). "Bayesian Assessment of the Distribution of Insurance Claim Counts using Reversible Jump MCMC", North American Actuarial Journal, 9, p. 90–108.

See Also

dnbinom and dpois.

Examples

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library(LaplacesDemon)
y <- rpois(100, 5)
lambda <- rpois(100, 5)
x <- dgpois(y, lambda, 0.5)

#Plot Probability Functions
x <- seq(from=0, to=20, by=1)
plot(x, dgpois(x,1,0.5), ylim=c(0,1), type="l", main="Probability Function",
     ylab="density", col="red")
lines(x, dlaplace(x,1,0.6), type="l", col="green")
lines(x, dlaplace(x,1,0.7), type="l", col="blue")
legend(2, 0.9, expression(paste(lambda==1, ", ", omega==0.5),
     paste(lambda==1, ", ", omega==0.6), paste(lambda==1, ", ", omega==0.7)),
     lty=c(1,1,1), col=c("red","green","blue"))

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