In probability and statistics, the generalized integer gamma distribution (GIG) is the distribution of the sum of independent
gamma distributed random variables, all with integer shape parameters and different rate parameters. This is a special case of the generalized chi-squared distribution. A related concept is the generalized near-integer gamma distribution (GNIG).
random variables, with all being positive integers and all different. In other words, each variable has the Erlang distribution with different shape parameters. The uniqueness of each shape parameter comes without loss of generality, because any case where some of the are equal would be treated by first adding the corresponding variables: this sum would have a gamma distribution with the same rate parameter and a shape parameter which is equal to the sum of the shape parameters in the original distributions.
Then the random variable Y defined by
has a GIG (generalized integer gamma) distribution of depth with shape parameters and rate parameters. This fact is denoted by
Alternative expressions are available in the literature on generalized chi-squared distribution, which is a field where computer algorithms have been available for some years.[when?]
Generalization
The GNIG (generalized near-integer gamma) distribution of depth is the distribution of the random variable[4]
where and are two independent random variables, where is a positive non-integer real and where .
Properties
The probability density function of is given by
and the cumulative distribution function is given by
where
with given by (1)-(3) above. In the above expressions is the Kummer confluent hypergeometric function. This function has usually very good convergence properties and is nowadays easily handled by a number of software packages.
Applications
The GIG and GNIG distributions are the basis for the exact and near-exact distributions of a large number of likelihood ratio test statistics and related statistics used in
multivariate analysis. [5][6][7][8][9] More precisely, this application is usually for the exact and near-exact distributions of the negative logarithm of such statistics. If necessary, it is then easy, through a simple transformation, to obtain the corresponding exact or near-exact distributions for the corresponding likelihood ratio test statistics themselves. [4][10][11]
The GIG distribution is also the basis for a number of wrapped distributions in the wrapped gamma family.
[12]