Add to ORKGBy means of the Malliavin Calculus, we derive asymptotic expansion of the probability distributions of statistics for systems perturbed by small noises. These results are applied to the problem of the second order asymptotic efficiency of the maximum likelihood estimator.maximum likelihood estimator Malliavin calculus asymptotic expansion (null)
AbstractWe proved the validity of the asymptotic expansion for the distribution of a martingale with...
We describe Monte Carlo approximation to the maximum likelihood estimator in models with intractabl...
We develop a technique based on Malliavin-Bismut calculus ideas, for asymptotic expansion of dual co...
AbstractBy means of the Malliavin Calculus, we derive asymptotic expansion of the probability distri...
In this dissertation we present several applications of Malliavin calculus, both to the statistical ...
In the present article, we will consider a conditional limit theorem and conditional asymptotic expa...
maximum likelihood estimator, asymptotic efficiency, stochastic partial differential equations,
Let {Zn}n≥1 be a sequence of random vectors. Under certain conditions, distributions of stat...
For a centered random variable X in a Wiener space, differentiable in the sense of Malliavin, the fu...
AbstractBy means of the Malliavin calculus, we present an expansion formula for the distribution of ...
We prove a general result on asymptotic expansions of densities for families of perturbed Wiener fu...
A Wiener model consists of a linear dynamic system followed by a static nonlinearity. The input and ...
AbstractWe consider an infinite-dimensional dynamical system with polynomial nonlinearity and additi...
We study the existence and properties of the density for the law of the solution to a nonlinear hype...
Using a representation as an infinite linear combination of chisquare independent random variables, ...
AbstractWe proved the validity of the asymptotic expansion for the distribution of a martingale with...
We describe Monte Carlo approximation to the maximum likelihood estimator in models with intractabl...
We develop a technique based on Malliavin-Bismut calculus ideas, for asymptotic expansion of dual co...
AbstractBy means of the Malliavin Calculus, we derive asymptotic expansion of the probability distri...
In this dissertation we present several applications of Malliavin calculus, both to the statistical ...
In the present article, we will consider a conditional limit theorem and conditional asymptotic expa...
maximum likelihood estimator, asymptotic efficiency, stochastic partial differential equations,
Let {Zn}n≥1 be a sequence of random vectors. Under certain conditions, distributions of stat...
For a centered random variable X in a Wiener space, differentiable in the sense of Malliavin, the fu...
AbstractBy means of the Malliavin calculus, we present an expansion formula for the distribution of ...
We prove a general result on asymptotic expansions of densities for families of perturbed Wiener fu...
A Wiener model consists of a linear dynamic system followed by a static nonlinearity. The input and ...
AbstractWe consider an infinite-dimensional dynamical system with polynomial nonlinearity and additi...
We study the existence and properties of the density for the law of the solution to a nonlinear hype...
Using a representation as an infinite linear combination of chisquare independent random variables, ...
AbstractWe proved the validity of the asymptotic expansion for the distribution of a martingale with...
We describe Monte Carlo approximation to the maximum likelihood estimator in models with intractabl...
We develop a technique based on Malliavin-Bismut calculus ideas, for asymptotic expansion of dual co...