We introduce the notion of covariance measure structure for square integrable stochas-tic processes. We dene Wiener integral, we develop a suitable formalism for stochastic calculus of variations and we make Gaussian assumptions only when necessary. Our main examples are nite quadratric variation processes with stationary increments and the bifractional Brownian motion. Key words and phrases: Square integrable processes, covariance measure structure, Malliavi
International audienceThis article focuses on a new concept of quadratic variation for processes tak...
In this Chapter, the basic concepts of stochastic integration are explained in a way that is readily...
We define a Skorohod type anticipative stochastic integral that extends the Ito integral not only wi...
We introduce the notion of covariance measure structure for square integrable sto-chastic processes....
International audienceWe introduce the notion of covariance measure structure for square integrable ...
AbstractWe introduce the notion of covariance measure structure for square integrable stochastic pro...
We develop a stochastic analysis for a Gaussian process $X$ with singular covariance by an intrinsic...
Cette thèse développe un formalisme intrinsèque de calcul stochastique de type Malliavin-Skorohod po...
International audienceA stochastic calculus similar to Malliavin's calculus is worked out for Browni...
Stochastic analysis is the analysis of functionals defined on the Wiener space, i.e., the space on w...
In this paper we study the structure of square integrable functionals measur-able with respect to co...
Examples of square integrable martingales adapted to processes with independent increments and ortho...
AbstractExamples of square integrable martingales adapted to processes with independent increments a...
The Malliavin calculus (or stochastic calculus of variations) is an infinite-dimensional differentia...
This volume presents an introductory course on differential stochastic equations and Malliavin calcu...
International audienceThis article focuses on a new concept of quadratic variation for processes tak...
In this Chapter, the basic concepts of stochastic integration are explained in a way that is readily...
We define a Skorohod type anticipative stochastic integral that extends the Ito integral not only wi...
We introduce the notion of covariance measure structure for square integrable sto-chastic processes....
International audienceWe introduce the notion of covariance measure structure for square integrable ...
AbstractWe introduce the notion of covariance measure structure for square integrable stochastic pro...
We develop a stochastic analysis for a Gaussian process $X$ with singular covariance by an intrinsic...
Cette thèse développe un formalisme intrinsèque de calcul stochastique de type Malliavin-Skorohod po...
International audienceA stochastic calculus similar to Malliavin's calculus is worked out for Browni...
Stochastic analysis is the analysis of functionals defined on the Wiener space, i.e., the space on w...
In this paper we study the structure of square integrable functionals measur-able with respect to co...
Examples of square integrable martingales adapted to processes with independent increments and ortho...
AbstractExamples of square integrable martingales adapted to processes with independent increments a...
The Malliavin calculus (or stochastic calculus of variations) is an infinite-dimensional differentia...
This volume presents an introductory course on differential stochastic equations and Malliavin calcu...
International audienceThis article focuses on a new concept of quadratic variation for processes tak...
In this Chapter, the basic concepts of stochastic integration are explained in a way that is readily...
We define a Skorohod type anticipative stochastic integral that extends the Ito integral not only wi...