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The XGamma distribution is a generated distribution from a mixture of Exponential and Gamma distributions. It is found that in many cases the XGamma has more flexibility than the Exponential distribution. In this paper we consider the sum…

Statistics Theory · Mathematics 2025-04-22 Therrar Kadri , Rahil Omairi , Khaled Smaili , Seifedine Kadry

Bairamov et al. (Aust N Z J Stat 47:543-547, 2005) characterize the exponential distribution in terms of the regression of a function of a record value with its adjacent record values as covariates. We extend these results to the case of…

Probability · Mathematics 2007-07-30 George P. Yanev , M. Ahsanullah , M. I. Beg

In this present work, we discuss the Bayesian inference for the bivariate pseudo-exponential distribution. Initially, we assume independent gamma priors and then pseudo-gamma priors for the pseudo-exponential parameters. We are primarily…

Methodology · Statistics 2023-06-27 Banoth Veeranna

We completely characterize $\Delta$- and local subexponentialities of positive-half compound Poisson distributions and extend the characterization on two-sided distributions. Moreover, $\Delta$-subexponentiality of infinitely divisible…

Probability · Mathematics 2023-02-21 Muneya Matsui , Toshiro Watanabe

In this paper, we obtain general representations for the joint distributions and copulas of arbitrary dependent random variables absolutely continuous with respect to the product of given one-dimensional marginal distributions. The…

Statistics Theory · Mathematics 2016-08-16 Victor H. de la Peña , Rustam Ibragimov , Shaturgun Sharakhmetov

Let $X$ and $Y$ be two independent random variables with corresponding distributions $F$ and $G$ supported on $[0,\infty)$. The distribution of the product $XY$, which is called the product convolution of $F$ and $G$, is denoted by $H$. In…

Probability · Mathematics 2019-01-08 Zhaolei Cui , Guancheng Jiang , Yuebao Wang

It is known that in many cases distributions of exponential integrals of Levy processes are infinitely divisible and in some cases they are also selfdecomposable. In this paper, we give some sufficient conditions under which distributions…

Statistics Theory · Mathematics 2012-11-26 Anita Behme , Makoto Maejima , Muneya Matsui , Noriyoshi Sakuma

In this paper some new characterizing theorems of exponential distribution based on order statistics are presented. Some existing results are generalized and the open conjecture by Arnold and Villasenor is solved.

Probability · Mathematics 2023-05-30 Bojana Milošević , Marko Obradović

The lognormal distribution describing, e.g., exponentials of Gaussian random variables is one of the most common statistical distributions in physics. It can exhibit features of broad distributions that imply qualitative departure from the…

Data Analysis, Statistics and Probability · Physics 2009-11-07 M. Romeo , V. Da Costa , F. Bardou

The aim of this paper, is to define a bivariate exponentiated generalized linear exponential distribution based on Marshall-Olkin shock model. Statistical and reliability properties of this distribution are discussed. This includes…

Statistics Theory · Mathematics 2017-10-03 Mohamed Ibrahim , M. S. Eliwa , M. El- Morshedy

The general relationship between an arbitrary frequency distribution and the expectation value of the frequency distributions of its samples is esablished. A set of combinations of expectation values whose value does not in general depend…

Data Analysis, Statistics and Probability · Physics 2012-10-05 Paolo Rossi

Multiplicative self-decomposable laws describe random variables that can be decomposed into a product of a scaled-down version of themselves and an independent residual term. Shanbhag et al.~(1977) have shown that the gamma distribution is…

Probability · Mathematics 2026-01-19 José Luís da Silva , Mohamed Erraoui

Let X and Y be two independent and nonnegative random variables with corresponding distributions F and G. Denote by H the distribution of the product XY , called the product convolution of F and G. Cline and Samorodnitsky (1994) proposed…

Probability · Mathematics 2017-10-03 Hui Xu , Fengyang Cheng , Yuebao Wang , Dongya Cheng

In probability theory, there is a tendency to treat one random variable with a given distribution as being just as good as any other. By and large this is fine because probability is (mostly) concerned with distributional properties of…

Probability · Mathematics 2013-01-31 Douglas Rizzolo

We consider the distribution of the sum and the maximum of a collection of independent exponentially distributed random variables. The focus is laid on the explicit form of the density functions (pdf) of non-i.i.d. sequences. Those are…

Probability · Mathematics 2013-07-16 Markus Bibinger

We study distributions $F$ on $[0,\infty)$ such that for some $T\le\infty$, $F^{*2}(x,x+T]\sim 2 F(x,x+T]$. The case $T=\infty$ corresponds to $F$ being subexponential, and our analysis shows that the properties for $T<\infty$ are, in fact,…

Probability · Mathematics 2013-03-20 S. Asmussen , S. Foss , D. Korshunov

In this letter, we give a concise, closed-form expression for the differential entropy of the sum of two independent, non-identically-distributed exponential random variables. The derivation is straightforward, but such a concise entropy…

Information Theory · Computer Science 2016-09-12 Andrew W. Eckford , Peter J. Thomas

We derive an asymptotic expansion for the distribution of a compound sum of independent random variables, all having the same light-tailed subexponential distribution. The examples of a Poisson and geometric number of summands serve as an…

Probability · Mathematics 2007-05-23 Ph . Barbe , W. P. McCormick , C. Zhang

The concern of this paper is a famous combinatorial formula known under the name "exponential formula". It occurs quite naturally in many contexts (physics, mathematics, computer science). Roughly speaking, it expresses that the exponential…

Discrete Mathematics · Computer Science 2010-11-04 L. Poinsot , G. H. E. Duchamp , S. Goodenough , K. A. Penson

`Distribution regression' refers to the situation where a response Y depends on a covariate P where P is a probability distribution. The model is Y=f(P) + mu where f is an unknown regression function and mu is a random error. Typically, we…

Machine Learning · Statistics 2013-02-04 Barnabas Poczos , Alessandro Rinaldo , Aarti Singh , Larry Wasserman