On tail probabilities and first passage times for fractional Brownian motion

In the paper we present a method of simulation of ruin probability over infinite horizon for fractional Brownian motion with parameter of self-similarity H >½. We derive some theoretical results which show how fast the method works. As an application of our method we numerically compute the Pickands constant.     

Local behaviour of first passage probabilities

Suppose that S is an asymptotically stable random walk with norming sequence c _n and that T _x is the time that S first enters (x, ∞), where x?≥ 0. The asymptotic behaviour of P(T ₀?=?n) has been described in a recent paper of Vatutin and Wachtel (Probab. Theory Relat. Fields 143:177–217, 2009), and here we build on that result to give three estimates for P(T _x ?=?n), which hold uniformly as n → ∞ in the regions x?=?o(c _n ), x?=?O(c _n ), and x/c _n → ∞, respectively.     

Importance sampling algorithms for first passage time probabilities in the infinite server queue

This paper applies importance sampling simulation for estimating rare event probabilities of the first passage time in the infinite server queue with renewal arrivals and general service time distributions. We consider importance sampling algorithms which are based on large deviations results of the infinite server queue, and we consider an algorithm based on the cross-entropy method, where we allow light-tailed and heavy-tailed distributions for the interarrival times and the service times. Efficiency of the algorithms is discussed by simulation experiments.     

Saddle point approximations to probabilities of sample mean deviations

Translated from Lietuvos Matematikos Rinkinys. Vol. 36, No. 2, pp. 245–256, April-June, 1996.     

Computation of failure probabilities via local approximations

For failure probability estimates of large structural systems, the numerical expensive evaluations of the limit state function have to be replaced by suitable approximations. Most of the methods proposed in the literature so far construct global approximations of the failure hypersurface. The global approximation of the failure hypersurface does not correspond to the local character of the most likely failure, which is often concentrated in one or several regions in the design space, and may therefore introduce a high approximation error for the probability of failure. Moreover, it is noted that global approximations are often constructed for parameter spaces that ignore constraints imposed by the physical nature of the problem. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Upper tail probabilities of integrated Brownian motions

We obtain new upper tail probabilities of m-times integrated Brownian motions under the uniform norm and the Lp norm. For the uniform norm, Talagrand’s approach is used, while for the Lp norm, Zolotare’s approach together with suitable metric entropy and the associated small ball probabilities are used. This proposed method leads to an interesting and concrete connection between small ball probabilities and upper tail probabilities(large ball probabilities) for general Gaussian random variables in Banach spaces. As applications,explicit bounds are given for the largest eigenvalue of the covariance operator, and appropriate limiting behaviors of the Laplace transforms of m-times integrated Brownian motions are presented as well.     

First passage probabilities of one-dimensional diffusion processes

This work is devoted to calculating the first passage probabilities of one-dimensional diffusion processes. For a one-dimensional diffusion process, we construct a sequence of Markov chains so that their absorption probabilities approximate the first passage probability of the given diffusion process. This method is especially useful when dealing with time-dependent boundaries.     

On moments based Padé approximations of ruin probabilities

In this paper, we investigate the quality of the moments based Padé approximation of ultimate ruin probabilities by exponential mixtures. We present several numerical examples illustrating the quick convergence of the method in the case of Gamma processes. While this is not surprising in the completely monotone case (which holds when the shape parameter is less than 1), it is more so in the opposite case, for which we improve even further the performance by a fix-up which may be of special importance due to its potential use in the four moments Gamma approximation.We also review the connection of the exponential mixtures approximation to Padé approximation, orthogonal polynomials, and Gaussian quadrature. These connections may turn out useful for providing rates of convergence.     

Finite approximations of data-based decision problems under imprecise probabilities

In decision theory under imprecise probabilities, discretizations are a crucial topic because many applications involve infinite sets whereas most procedures in the theory of imprecise probabilities can only be calculated for finite sets so far. The present paper develops a method for discretizing sample spaces in data-based decision theory under imprecise probabilities. The proposed method turns an original decision problem into a discretized decision problem. It is shown that any solution of the discretized decision problem approximately solves the original problem.In doing so, it is pointed out that the commonly used method of natural extension can be most instable. A way to avoid this instability is presented which is sufficient for the purpose of the paper.     

Gaussian approximations of Brownian motion in a stochastic integral

Given a Brownian motionB, Gaussian approximationsB , >0, of the form B _t = ₀ ^t K (u, s)dB _s du, t O , including polygonal and mollifier approximations, are considered. A limit theorem is proved for the integrals _T ⁰ X _t dB ^t as O. In particular, in the case of symmetric kernelsK the limit is the Fisk-Stratonovich integral ₀ ^t X _t odB _t.Vilnius University, Naugarduko 24, 2006 Vilnius, Lithuania. Vilnius Pedagogical University, Studentu 39, 2034 Vilnius, Lithuania. Translated from Lietuvos Matematikos Rinkinys, Vol. 33, No. 4, pp. 508–526, October–December, 1993. Translated by V. Mackeviius     

Some first passage problems related to cusum procedures

Let $\text{X}{}_1\text{,}\text{X}{}_2\text{,…}$ be independent random variables, and set W_n = max(0,W_n-1 + X_n), W₀ = 0, n ? 1. The so-called cusum (cumulative sum) procedure uses the first passage time T(h) = inf{n ? 1: W_n?h}for detecting changes in the mean μ of the process. It is shown that lim_h→∞ μET(h)/h = 1 if μ > 0. Also, a cusum procedure for detecting changes in the normal mean is derived when the variance is unknown. An asymptotic approximation to the average run length is given.     

Ten penalisation results of Brownian motion involving its one-sided supremum until first and last passage times, VIII

We penalise Brownian motion by a function of its one-sided supremum considered up to the last zero before t, respectively first zero after t, of that Brownian motion. This study presents some analogy with penalisation by the longest length of Brownian excursions, up to time t.     

Hitting probabilities and fractal dimensions of multiparameter multifractional Brownian motion

The main goal of this paper is to study the sample path properties for the harmonisable-type N-parameter multifractional Brownian motion, whose local regularities change as time evolves. We provide the upper and lower bounds on the hitting probabilities of an （N, d）-multifractional Brownian motion. Moreover, we determine the Hausdorff dimension of its inverse images, and the Hausdorff and packing dimensions of its level sets.     

Continuous collocation approximations to solutions of first kind Volterra equations

In this paper we give necessary and sufficient conditions for convergence of continuous collocation approximations of solutions of first kind Volterra integral equations. The results close some longstanding gaps in the theory of polynomial spline collocation methods for such equations. The convergence analysis is based on a Runge-Kutta or ODE approach.

     

A first–passage time random walk distribution with five transition probabilities: a generalization of the shifted inverse trinomial

In this paper a univariate discrete distribution, denoted by GIT, is proposed as a generalization of the shifted inverse trinomial distribution, and is formulated as a first-passage time distribution of a modified random walk on the half-plane with five transition probabilities. In contrast, the inverse trinomial arises as a random walk on the real line with three transition probabilities. The probability mass function (pmf) is expressible in terms of the Gauss hypergeometric function and this offers computational advantage due to its recurrence formula. The descending factorial moment is also obtained. The GIT contains twenty-two possible distributions in total. Special cases include the binomial, negative binomial, shifted negative binomial, shifted inverse binomial or, equivalently, lost-games, and shifted inverse trinomial distributions. A subclass GIT_3,1 is a particular member of Kemp’s class of convolution of pseudo-binomial variables and its properties such as reproductivity, formulation, pmf, moments, index of dispersion, and approximations are studied in detail. Compound or generalized (stopped sum) distributions provide inflated models. The inflated GIT_3,1 extends Minkova’s inflated-parameter binomial and negative binomial. A bivariate model which has the GIT as a marginal distribution is also proposed.     

Bounds and approximations for continuous-time Markovian transition probabilities and large systems

We propose new bounds and approximations for the transition probabilities of a continuous-time Markov process with finite but large state-space. The bounding and approximating procedures have been exposed in another paper [S. Mercier, Numerical bounds for semi-Markovian quantities and applications to reliability, in revision for Methodology and Computing in Applied Probability] in the more general context of a continuous-time semi-Markov process with countable state-space. Such procedures are here specialized to the Markovian finite case, leading to much simpler algorithms. The aim of this paper is to test such algorithms versus other algorithms from the literature near from ours, such as forward Euler approximation, external uniformization and a finite volume method from [C. Cocozza-Thivent, R. Eymard, Approximation of the marginal distributions of a semi-Markov process using a finite volume scheme, ESAIM: M2AN 38(5) (2004) 853–875].