Self-similar processes are stochastic processes that are invariant in distribution under
suitable time scaling, and are a subject intensively studied in the last few decades. This …

Stochastic processes are widely used for model building in the social, physical, engineering
and life sciences as well as in financial economics. In model building, statistical inference for …

In this paper, we study the asymptotic properties for the drift parameter estimators in the
fractional Ornstein-Uhlenbeck process with periodic mean function and long range …

We combine Malliavin calculus with Stein's method, in order to derive explicit bounds in the
Gaussian and Gamma approximations of random variables in a fixed Wiener chaos of a …

The concept of Wiener chaos generalizes to an infinite-dimensional setting the properties of
orthogonal polynomials associated with probability distributions on the real line. It plays a …

We study a least squares estimator θ̂T for the Ornstein–Uhlenbeck process, dXt= θXtdt+
σdBtH, driven by fractional Brownian motion BH with Hurst parameter H≥ 12. We prove the …

This book provides the reader with the principal concepts and results related to differential
properties of measures on infinite dimensional spaces. In the finite dimensional case such …

This paper studies the least squares estimator (LSE) for the drift parameter of an Ornstein–
Uhlenbeck process driven by fractional Brownian motion, whose observations can be made …

'Written by a well-known expert in fractional stochastic calculus, this book offers a
comprehensive overview of Gaussian analysis, with particular emphasis on nonlinear …

We develop connections between Stein's approximation method, logarithmic Sobolev and
transport inequalities by introducing a new class of functional inequalities involving the …