Application of parameter estimation and hypothesis test for a generalized gamma distribution

Abstract

This paper develops maximum likelihood (ML) estimation procedures for the parameters of a generalized Gamma probability density function. In addition, the likelihood ratio conditioned on the ML estimates of the process parameters is given. A special case arising in the analysis of carbon monoxide pollution data is discussed in detail and the performance of the binary hypothesis test functioning as a pollution forecasting algorithm is reported.