The purpose of this paper is to study the mean, the variance, the probability distribution and the hazard rate of the inverse range process of an a-priori unknown volatility random walk. Motivation for this process arises when it is necessary to obtain statistics that pertain to a process...

The purpose of this paper is to introduce and construct a state dependent counting and persistent random walk. Persistence is imbedded in a Markov chain for predicting insured claims based on their current and past period claim. We calculate for such a process, the probability generating...

This paper considers a memory-based persistent counting random walk, based on a Markov memory of the last event. This persistent model is a different than the Weiss persistent random walk model however, leading thereby to different results. We point out to some preliminary result, in particular,...

This paper introduces a definition of reliability based on a process range. Thus, process failure is defined when the range of a process first reaches a given and unacceptable level. The Mean Time To Failure (MTTF) which is denned as the mean of the first time for a range to attain a given...

This paper introduces a definition of reliability based on a process range. Thus, process failure is defined when the range of a process first reaches a given and unacceptable level. The Mean Time To Failure (MTTF) which is denned as the mean of the first time for a range to attain a given...

We work out a stationary process on the real line to represent the positions of the multiple cracks which are observed in some composites materials submitted to a fixed unidirectional stress [var epsilon]. Our model is the one-dimensional random sequential adsorption. We calculate the intensity...

Let M be a normal martingale (i.e. <M, M> (t) = t), we decompose the product of two multiple stochastic integrals (with respect to M) In(f)Im(g) as a sum of n [logical and] m terms Hk. Hk is equal to the integral over k+ of the function t -- In+m-2k(hk(t,.)), with respect to the k-tensor product of...</m,>

If X and Y are two general stochastic processess, we define a covariation process [X, Y] with the help of a limit procedure. When the processes are semimartingales, [X, Y] is their classical bracket. We calculate covariation for some important examples arising from anticipating stochastic...

Let (Xn)n[greater-or-equal, slanted]1 be a sequence of real random variables. The local score is Hn=max1[less-than-or-equals, slant]i<j[less-than-or-equals, slant]n (Xi+...+Xj). If (Xn)n[greater-or-equal, slanted]1 is a "good" Markov chain under its invariant measure, the Xi are centered, we prove that converges in distribution to B1* when n-->+[infinity], where B1*=max0[less-than-or-equals, slant]u[less-than-or-equals, slant]1 Bu and (Bu,u[greater-or-equal, slanted]0) is a standard Brownian motion,...</j[less-than-or-equals,>

We calculate the density function of (U∗(t),θ∗(t)), where U∗(t) is the maximum over [0,g(t)] of a reflected Brownian motion U, where g(t) stands for the last zero of U before t, θ∗(t)=f∗(t)−g∗(t), f∗(t) is the hitting time of the level U∗(t), and g∗(t) is the left-hand...