The compound Poisson-gamma variable is the sum of a random sample from a gamma distribution with sample size an independent Poisson random variable. It has received wide ranging applications. In this note, we give an account of its mathematical properties including estimation procedures by the methods of moments and maximum likelihood. Most of the properties given are hitherto unknown.
We give a stochastic expansion for estimates that minimise the arithmetic mean of (typically independent) random functions of a known parameter. Examples include least squares estimates, maximum likelihood estimates and more generally -estimates. This is used to obtain leading cumulant coefficients of needed for the Edgeworth expansions for the distribution and density ) to magnitude (or to for the symmetric case),...
We extend Leibniz' rule for repeated derivatives of a product to multivariate integrals of a product. As an application we obtain expansions for ( < < ) for ~ (0,) and for repeated integrals of the density of . When > 0 in the expansion for ( < ) reduces to one given by [H. Ruben B 68 (1964) 3–11]. in terms of the moments of (0, ). This is shown to be a special case of an expansion in...
Consider testing : ∈ against : ∈ for a random sample , ..., from , where and are two disjoint sets of cdfs on ℝ = (−∞, ∞). Two non-local types of efficiencies, referred to as the fixed- and fixed- efficiencies, are introduced for this two-hypothesis testing situation. Theoretical tools are developed to evaluate these efficiencies for some of the most usual goodness...
We extend Leibniz' rule for repeated derivatives of a product to multivariate integrals of a product. As an application we obtain expansions for ( < < ) for ~ (0,) and for repeated integrals of the density of . When > 0 in the expansion for ( < ) reduces to one given by [H. Ruben B (1964) 3–11]. in terms of the moments of (0, ). This is shown to be a special case of an expansion in terms of the multivariate...
The five-parameter generalized gamma distribution is one of the most flexible distributions in statistics. In this note, for the first time, we provide asymptotic covariances for the parameters using both the method of maximum likelihood and the method of moments.
Consider testing whether for a continuous cdf on = (-∞,∞) and for a random sample ,..., from . We derive expansions of the associated asymptotic power based on the Cramer-von Mises, Kolmogorov-Smirnov and Kuiper statistics. We provide numerical illustrations using a double-exponential example with a shifted alternative.
We give a stochastic expansion for estimates
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