Gamma distribution nests exponential, chi-squared and Erlang distributions;while generalized Inverse Gaussian distribution nests quite a number of distributions. The aim of this paper is to construct a gamma mixture u...Gamma distribution nests exponential, chi-squared and Erlang distributions;while generalized Inverse Gaussian distribution nests quite a number of distributions. The aim of this paper is to construct a gamma mixture using Generalized inverse Gaussian mixing distribution. The </span><i><span style="font-family:Verdana;">rth</span></i><span style="font-family:Verdana;"> moment of the mixture is obtained via the </span><i><span style="font-family:Verdana;">rth</span></i><span style="font-family:Verdana;"> moment of the mixing distribution. Special cases and limiting cases of the mixture are deduced.展开更多
文摘Gamma distribution nests exponential, chi-squared and Erlang distributions;while generalized Inverse Gaussian distribution nests quite a number of distributions. The aim of this paper is to construct a gamma mixture using Generalized inverse Gaussian mixing distribution. The </span><i><span style="font-family:Verdana;">rth</span></i><span style="font-family:Verdana;"> moment of the mixture is obtained via the </span><i><span style="font-family:Verdana;">rth</span></i><span style="font-family:Verdana;"> moment of the mixing distribution. Special cases and limiting cases of the mixture are deduced.
