A generalization of the classical Ornstein Uhlenbeck diffusion process including some deterministic time dependent functions in the infinitesimal moments is considered. The inference based on discrete sampling in time is provided by means of an iterative procedure that, in each step, combines the classical maximum likelihood estimation and a generalized method of moments. The validity of the suggested procedure is evaluated via a simulation study by considering several infinitesimal moments for the Ornstein Uhlenbeck type process and taking different sample size. Finally, an application to PM10 daily concentration in Turin metropolitan area in Italy is discussed
International audienceWe study the estimation of parameters theta = (mu, sigma (2)) for a diffusion ...
AbstractWe consider the problem of estimating an unknown parameter m in case one observes in an inte...
Two methods of modeling for the Ornstein-Uhlenbeck process are studied in the work. This process has...
A generalization of the classical Ornstein Uhlenbeck diffusion process including some deterministic ...
Abstract We consider nonparametric estimation of the Lévy measure of a hidden Lévy process driving a...
We consider nonparametric estimation of the Lévy measure of a hidden Lévy process driving a stationa...
In this dissertation, we show with plausible arguments that the Stochastic Differential Equations (S...
In this Dissertation, we show with plausible arguments that the Stochastic Differential Equations (S...
In the paper we propose a stochastic model, based on a Vasicek non-homogeneous diffusion process, in...
In this thesis, the extension of the Ornstein-Uhlenbeck process is studied by first driving this mo...
In this article we propose a maximum likelihood methodology to estimate the parameters of a one-dime...
We solve a physically significant extension of a classic problem in the theory of diffusion, namely ...
The goal of this thesis is to estimate parameters in a bidimensional Ornstein-Uhlenbeck process, nam...
General methods to simulate probability density functions and first passage time densities are provi...
The Ornstein-Uhlenbeck process has countless practical applications most of which rely on having pre...
International audienceWe study the estimation of parameters theta = (mu, sigma (2)) for a diffusion ...
AbstractWe consider the problem of estimating an unknown parameter m in case one observes in an inte...
Two methods of modeling for the Ornstein-Uhlenbeck process are studied in the work. This process has...
A generalization of the classical Ornstein Uhlenbeck diffusion process including some deterministic ...
Abstract We consider nonparametric estimation of the Lévy measure of a hidden Lévy process driving a...
We consider nonparametric estimation of the Lévy measure of a hidden Lévy process driving a stationa...
In this dissertation, we show with plausible arguments that the Stochastic Differential Equations (S...
In this Dissertation, we show with plausible arguments that the Stochastic Differential Equations (S...
In the paper we propose a stochastic model, based on a Vasicek non-homogeneous diffusion process, in...
In this thesis, the extension of the Ornstein-Uhlenbeck process is studied by first driving this mo...
In this article we propose a maximum likelihood methodology to estimate the parameters of a one-dime...
We solve a physically significant extension of a classic problem in the theory of diffusion, namely ...
The goal of this thesis is to estimate parameters in a bidimensional Ornstein-Uhlenbeck process, nam...
General methods to simulate probability density functions and first passage time densities are provi...
The Ornstein-Uhlenbeck process has countless practical applications most of which rely on having pre...
International audienceWe study the estimation of parameters theta = (mu, sigma (2)) for a diffusion ...
AbstractWe consider the problem of estimating an unknown parameter m in case one observes in an inte...
Two methods of modeling for the Ornstein-Uhlenbeck process are studied in the work. This process has...