Probability density function
The generalized gamma distribution is a continuous probability distribution with three parameters. It is a generalization of the two-parameter gamma distribution. Since many distributions commonly used for parametric models in survival analysis (such as the exponential distribution, the Weibull distribution and the gamma distribution) are special cases of the generalized gamma, it is sometimes used to determine which parametric model is appropriate for a given set of data. Another example is the half-normal distribution.
The generalized gamma distribution has three parameters: , , and . For non-negative x from a generalized gamma distribution, the probability density function is
where denotes the gamma function.
The cumulative distribution function is
where denotes the lower incomplete gamma function.
The quantile function can be found by noting that where is the cumulative distribution function of the gamma distribution with parameters and . The quantile function is then given by inverting using known relations about inverse of composite functions, yielding:
with being the quantile function for a gamma distribution with .
If X has a generalized gamma distribution as above, then
Denote GG(a,d,p) as the generalized gamma distribution of parameters a, d, p.
Then, given and two positive real numbers, if , then
If and are the probability density functions of two generalized gamma distributions, then their Kullback-Leibler divergence is given by
where is the digamma function.
In the R programming language, there are a few packages that include functions for fitting and generating generalized gamma distributions. The gamlss package in R allows for fitting and generating many different distribution families including generalized gamma (family=GG). Other options in R, implemented in the package flexsurv, include the function dgengamma, with parameterization: , , , and in the package ggamma with parametrisation , , .