Mathematics Scholarly Works: Recent submissions
Now showing items 141-160 of 283
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Markov Field Property of Stochastic Differential Equations
(Institute of Mathematical Statistics (IMS), 1995-03-01)The purpose of this paper is to prove a characterization of the conditional independence of two independent random variables given a particular functional of them, in terms of a factorization property. As an application ... -
Points of Positive Density for Smooth Functionals
(Institute of Mathematical Statistics (IMS), 1998-12-03)In this paper we show that the set of points where the density of a Wiener functional is strictly positive is an open connected set, assuming some regularity conditions. -
Stochastic evolution equations with random generators
(Institute of Mathematical Statistics (IMS), 1998-05-01)We prove the existence of a unique mild solution for a stochastic evolution equation on a Hilbert space driven by a cylindrical Wiener process. The generator of the corresponding evolution system is supposed to be random ... -
Evolution equation of a stochastic semigroup with white-noise drift
(Institute of Mathematical Statistics (IMS), 2000-09-20)We study the existence and uniqueness of the solution of a function-valued stochastic evolution equation based on a stochastic semigroup whose kernel p(s,t,y,x) is Brownian in s and t.The kernel p is supposed to be measurable ... -
Stochastic Calculus with Respect to Gaussian Processes
(Institute of Mathematical Statistics, 2001-12-05)In this paper we develop a stochastic calculus with respect to a Gaussian process of the form Bt=∫t0K(t,s)dWs, where W is a Wiener process and K(t,s) is a square integrable kernel, using the techniques of the stochastic ... -
Smoothness of the law of the supremum of the fractional Brownian motion
(Institute of Mathematical Statistics, 2003-07-25)This note is devoted to prove that the supremum of a fractional Brownian motion with Hurst parameter H∈(0,1) has an infinitely differentiable density on (0,∞). The proof of this result is based on the techniques of the ... -
Probabilistic models for vortex filaments based on fractional Brownian motion
(Institute of Mathematical Statistics (IMS), 2003-11-01)We consider a vortex structure based on a three-dimensional fractional Brownian motion with Hurst parameter H>12. We show that the energy H\vspace*{-1pt} has moments of any order under suitable conditions. When H∈(12,13) ... -
Renormalized self-intersection local time for fractional Brownian motion
(Institute of Mathematical Statistics, 2005-05-06) -
Central limit theorems for sequences of multiple stochastic integrals
(Institute of Mathematical Statistics (IMS), 2005-01-01)We characterize the convergence in distribution to a standard normal law for a sequence of multiple stochastic integrals of a fixed order with variance converging to 1. Some applications are given, in particular to study ... -
Notes on the two-dimensional fractional Brownian motion
(Institute of Mathematical Statistics, 2006-02-17)We study the two-dimensional fractional Brownian motion with Hurst parameter H>½. In particular, we show, using stochastic calculus, that this process admits a skew-product decomposition and deduce from this representation ... -
Regularity of the density for the stochastic heat equation
(Institute of Mathematical Statistics (IMS), 2008-12-18)We study the smoothness of the density of a semilinear heat equation with multiplicative spacetime white noise. Using Malliavin calculus, we reduce the problem to a question of negative moments of solutions of a linear ... -
Rough path analysis via fractional calculus
(American Mathematical Society, 2009-11-02) -
Stochastic integral representation of the L2 modulus of Brownian local time and a central limit theorem
(Institute of Mathematical Statistics (IMS), 2009-11-09)The purpose of this note is to prove a central limit theorem for the L2-modulus of continuity of the Brownian local time obtained in [3], using techniques of stochastic analysis. The main ingredients of the proof are an ... -
Fractional martingales and characterization of the fractional Brownian motion
(Institute of Mathematical Statistics, 2009-11-19)In this paper we introduce the notion of fractional martingale as the fractional derivative of order α of a continuous local martingale, where α∈(−½, ½), and we show that it has a nonzero finite variation of order 2/(1+2α), ... -
Central limit theorem for the third moment in space of the Brownian local time increments
(Institute of Mathematical Statistics (IMS), 2010-09-14)The purpose of this note is to prove a central limit theorem for the third integrated moment of the Brownian local time increments using techniques of stochastic analysis. The main ingredients of the proof are an asymptotic ... -
Limit theorems for nonlinear functionals of Volterra processes via white noise analysis
(Bernoulli Society for Mathematical Statistics and Probability, 2010-11-10)By means of white noise analysis, we prove some limit theorems for nonlinear functionals of a given Volterra process. In particular, our results apply to fractional Brownian motion (fBm) and should be compared with the ... -
Central and non-central limit theorems for weighted power variations of fractional Brownian motion
(Annals of the Institute Henri Poincaré, 2010-10-01)n this paper, we prove some central and non-central limit theorems for renormalized weighted power variations of order q≥2 of the fractional Brownian motion with Hurst parameter H∈(0, 1), where q is an integer. The central ... -
Feynman-Kac formula for the heat equation driven by fractional white noise
(Institute of Mathematical Statistics, 2011-02-01)We establish a version of the Feynman–Kac formula for the multidimensional stochastic heat equation with a multiplicative fractional Brownian sheet. We use the techniques of Malliavin calculus to prove that the process ... -
A construction of the rough path above fractional Brownian motion using Volterra’s representation
(Institute of Mathematical Statistics, 2011-09-01)This note is devoted to construct a rough path above a multidimensional fractional Brownian motion B with any Hurst parameter H∈(0, 1), by means of its representation as a Volterra Gaussian process. This approach yields ... -
Malliavin calculus for backward stochastic differential equations and applications to numerical solutions
(Institute of Mathematical Statistics, 2011-04-01)In this paper we study backward stochastic differential equations with general terminal value and general random generator. In particular, we do not require the terminal value be given by a forward diffusion equation. The ...