On a stochastic process with a heavy-tailed distributed component describing inventory model type of (s,S)

In the present study, the stochastic process X(t) describing inventory model type of (s, S) with a heavy-tailed distributed demands is considered. The asymptotic expansions at sufficiently large values of parameter β = S ? s for the ergodic distribution and nth-order moment of the process X(t) based on the main results of the studies Teugels (1968 Teugels, J.L. (1968). Renewal theorems when the first or the second moment is infinite. Ann. Math. Stat. 39(4):1210–1219.[Crossref] , [Google Scholar]) and Geluk and Frenk (2011 Geluk, J.L., Frenk, J.B.G. (2011). Renewal theory for random variables with a heavy tailed distribution and finite variance. Stat. Probab. Lett. 81:77–82.[Crossref], [Web of Science ®] , [Google Scholar]) are obtained.     

On the Moments of a Semi-Markovian Random Walk with Gaussian Distribution of Summands

In this article, a semi-Markovian random walk with delay and a discrete interference of chance (X(t)) is considered. It is assumed that the random variables ζ _n , n = 1, 2,…, which describe the discrete interference of chance form an ergodic Markov chain with ergodic distribution which is a gamma distribution with parameters (α, λ). Under this assumption, the asymptotic expansions for the first four moments of the ergodic distribution of the process X(t) are derived, as λ → 0. Moreover, by using the Riemann zeta-function, the coefficients of these asymptotic expansions are expressed by means of numerical characteristics of the summands, when the process considered is a semi-Markovian Gaussian random walk with small drift β.     

Asymptotic approach for a renewal-reward process with a general interference of chance

ABSTRACT

In this study, a renewal-reward process with a discrete interference of chance is constructed and considered. Under weak conditions, the ergodicity of the process X(t) is proved and exact formulas for the ergodic distribution and its moments are found. Within some assumptions for the discrete interference of chance in general form, two-term asymptotic expansions for all moments of the ergodic distribution are obtained. Additionally, kurtosis coefficient, skewness coefficient, and coefficient of variation of the ergodic distribution are computed. As a special case, a semi-Markovian inventory model of type (s, S) is investigated.