We consider the classical estimation problem of an unknown drift parameter within classes of nondegenerate diffusion processes. Using rough path theory (in the sense of T. Lyons), we analyze the Maximum Likelihood Estimator (MLE) with regard to its pathwise stability properties as well as robustness toward misspecification in volatility and even the very nature of the noise. Two numerical examples demonstrate the practical relevance of our results
The transition density of a diffusion process does not admit an explicit expression in general, whic...
AbstractWe study the problem of parameter estimation using maximum likelihood for fast/slow systems ...
We develop infinitesimally robust statistical procedures for general diffusion processes. We first p...
We consider the estimation problem of an unknown drift parameter within classes of non-degenerate di...
In this article we consider diffusion approximations for a general class of random recursions. Such ...
We study the maximum likelihood estimator of the drift parameters of a stochastic differential equat...
We consider the rough differential equation with drift driven by a Gaussian geometric rough path. Un...
This thesis is composed of two parts. The first part is devoted to inference for discretely observed...
The purpose of this paper is to study some statistical problems: parameter estimation, binary detect...
This paper introduces a Monte Carlo method for maximum likelihood inference in the context of discre...
AbstractThe maximum likelihood estimation of the unknown parameter of a diffusion process based on a...
We discuss a general likelihood formula for the estimation of parameters in diffusion processes. The...
We Study the problem of parameter estimation using maximum likelihood for fast/slow systems of stoch...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
We study the problem of parameter estimation using maximum likelihood for fast/slow systems of stoch...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
AbstractWe study the problem of parameter estimation using maximum likelihood for fast/slow systems ...
We develop infinitesimally robust statistical procedures for general diffusion processes. We first p...
We consider the estimation problem of an unknown drift parameter within classes of non-degenerate di...
In this article we consider diffusion approximations for a general class of random recursions. Such ...
We study the maximum likelihood estimator of the drift parameters of a stochastic differential equat...
We consider the rough differential equation with drift driven by a Gaussian geometric rough path. Un...
This thesis is composed of two parts. The first part is devoted to inference for discretely observed...
The purpose of this paper is to study some statistical problems: parameter estimation, binary detect...
This paper introduces a Monte Carlo method for maximum likelihood inference in the context of discre...
AbstractThe maximum likelihood estimation of the unknown parameter of a diffusion process based on a...
We discuss a general likelihood formula for the estimation of parameters in diffusion processes. The...
We Study the problem of parameter estimation using maximum likelihood for fast/slow systems of stoch...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
We study the problem of parameter estimation using maximum likelihood for fast/slow systems of stoch...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
AbstractWe study the problem of parameter estimation using maximum likelihood for fast/slow systems ...
We develop infinitesimally robust statistical procedures for general diffusion processes. We first p...