A note on the Bayesian inference for generalized multivariate gamma distribution

In this paper, we show that the derivation of Lemma 3 of Das and Dey (2010) needs to be corrected by using a logical transformation, instead of the ad-hoc transformation which is partially motivated by its univariate equivalent transformation. The correct derivation is presented by two approaches.     

On the maximum likelihood estimator for the Generalized Extreme-Value distribution

The vanilla method in univariate extreme-value theory consists of fitting the three-parameter Generalized Extreme-Value (GEV) distribution to a sample of block maxima. Despite claims to the contrary, the asymptotic normality of the maximum likelihood estimator has never been established. In this paper, a formal proof is given using a general result on the maximum likelihood estimator for parametric families that are differentiable in quadratic mean but whose supports depend on the parameter. An interesting side result concerns the (lack of) differentiability in quadratic mean of the GEV family.     

On certain generalized incomplete gamma functions

Recently, Chaudhry and Zubair have introduced a generalized incomplete gamma function Γ(v,x;z) which reduces to the incomplete gamma function Γ(v,x) when its variable z vanishes. We show that Γ(v,x;z) may be written essentially as a single Kampé de Fériet function which in turn may be expressed as a linear combination of two incomplete Weber integrals. Then by using properties of the latter integrals we deduce additional representations for Γ(v,x;z). In particular, we show that Γ(v,x;z) is essentially completely determined by a finite number of modified Bessel functions for all v ≠ 0 provided we know the values of the two incomplete Weber integrals when 0 < Re v ⩽ 1. When v = 0 we derive connections between the generalized incomplete gamma function and incomplete Lipschitz-Hankel integrals, and indicate that there exist connections with other special functions.     

基于对数正态分布下联合均值与散度广义线性模型的极大似然估计

基于对数正态分布研究提出了联合均值与散度广义线性模型,给出了此模型参数的极大似然估计,模拟和实例显示该模型和方法是有用和有效的.     

双重广义线性模型的经验似然推断

基于截面经验似然方法,将双重广义线性模型的拟似然估计方程作为截面经验似然比函数的约束条件,构造了均值模型和散度模型未知参数的置信区间.最后通过数据模拟,将该方法与正态逼近方法比较,说明了该方法是有效和可行的.     

On a generalized Eulerian distribution

The distribution with probability function p _k(n, , ) = A _{n, k}(, )/(+ )^[p], k = 0, 1, 2, ..., n, where the parameters and are positive real numbers, A _{n, k} (, ) is the generalized Eulerian number and ( + )^[n] = ( + )( + +1) ... ( + +n – 1), introduced and discussed by Janardan (1988, Ann. Inst. Statist. Math., 40, 439–450), is further studied. The probability generating function of the generalized Eulerian distribution is expressed by a generalized Eulerian polynomial which, when expanded suitably, provides the factorial moments in closed form in terms of non-central Stirling numbers. Further, it is shown that the generalized Eulerian distribution is unimodal and asymptotically normal.     

The log-linear model with a generalized gamma distribution for the error: A Bayesian approach

Considering a log-linear model with one covariate and a generalized gamma distribution for the error, we find the posterior densities for the parameters of interest. Since many standard survival distributions are particular cases of the generalized gamma model, the proposed bayesian method is very useful to discriminate between possible models to be used in the data analysis. The Laplace approximation for integrals (see Tierney and Kadane, 1984) is used to find the posterior distributions of the parameters involved when they cannot be obtained explicitly.     

On Bartlett correctability of empirical likelihood in generalized power divergence family

Baggerly (1998) showed that empirical likelihood is the only member in the Cressie–Read power divergence family to be Bartlett correctable. This paper strengthens Baggerly’s result by showing that in a generalized class of the power divergence family, which includes the Cressie–Read family and other nonparametric likelihood such as Schennach’s (2005, 2007) exponentially tilted empirical likelihood, empirical likelihood is still the only member to be Bartlett correctable.     

The distribution of the likelihood ratio for additive processes

Let {X(t), 0 ≤ t ≤ T} and {Y(t), 0 ≤ t ≤ T} be two additive processes over the interval [0, T] which, as measures over D[0, T], are absolutely continuous with respect to each other. Let μ_X and μ_Y be the measures over D[0, T] determined by the two processes. The characteristic function of $\text{ln}\text{(}\text{dμ}{}_{\mathrm{X}}\text{dμ}{}_{\mathrm{Y}}\text{)}$ with respect to μ_Y is obtained in terms of the determining parameters of the two processes.     

Inequalities for the median of the gamma distribution

We provide several new inequalities involving λ_n, the median of the gamma distribution of order n+1 with parameter 1. Among others, we present sharp upper and lower bounds for the arithmetic mean of λ₁,λ₂,…,λ_n. For all integers n?1 we have

     

On generalized gamma functions related to the Laurent coefficients of the Riemann zeta function

Summary We study a class of generalized gamma functions _k (z) which relate to the generalized Euler constants _k (basically the Laurent coefficients of(s)) as (z) does to the Euler constant. A new series expansion for _k is derived, and the constant term in the asymptotic expansion for log _k (z) is studied in detail. These and related constants are numerically computed for 1 k 15.