We consider a one-dimensional diffusion process (Xt) which is observed at n + 1 discrete times with regular sampling interval âˆ†. Assuming that (Xt) is strictly stationary, we propose nonparametricâ€¦ (More)

We consider the problem of estimating the stationary density of the process Vt in the stochastic volatility model dYt = âˆš VtdWt where Wt is a standard Brownian motion and Vt a Markov stationaryâ€¦ (More)

We consider N independent stochastic processes (Xi(t), t âˆˆ [0, Ti]), i = 1, . . . , N , defined by a stochastic differential equation with drift term depending on a random variable Ï†i. Theâ€¦ (More)

We consider N independent stochastic processes (Xj(t), t âˆˆ [0, T ]), j = 1, . . . , N , defined by a one-dimensional stochastic differential equation with coefficients depending on a random variableâ€¦ (More)

We consider a diffusion model of small variance type with positive drift density varying in a nonparametric set. We investigate Gaussian and Poisson approximations to this model in the sense ofâ€¦ (More)

Computable infinite dimensional filters with applications to discretized diffusion processes. Abstract Let us consider a pair signal-observation ((x n , y n), n â‰¥ 0) where the unobserved signal (x n)â€¦ (More)