AbstractAn asymptotic analysis is presented for estimation in the three-parameter Ornstein-Uhlenbeck process, where the parameters are the local mean, the drift, and the variance. We are interested in the case when the damping parameter (λ, or λT = κ) is nearly zero. The asymptotic sufficient statistics can be related to noncentral χ12 distribution. The maximum likelihood estimate of the parameter vector is a solution of a rather complicated system of equations. We describe the methods for solving maximum-likelihood equations. Classical and robust estimators are determined for parameters. It is shown that the lower confidence limit of the drift (or damping) parameter is equal to zero with positive probability when it is near to zero
In this thesis, the extension of the Ornstein-Uhlenbeck process is studied by first driving this mo...
We study the problem of parameter estimation for generalized Ornstein-Uhlenbeck processes with small...
International audienceWe investigate the asymptotic behavior of the maximum likelihood estimators of...
AbstractAn asymptotic analysis is presented for estimation in the three-parameter Ornstein-Uhlenbeck...
AbstractThe exact distribution of the maximum-likelihood estimator of the drift (damping) parameter ...
The Ornstein-Uhlenbeck process has countless practical applications most of which rely on having pre...
This paper considers parameter estimation in the Ornstein–Uhlenbeck process observed in the presence...
This paper considers parameter estimation in the Ornstein–Uhlenbeck process observed in the presence...
International audienceWe investigate the asymptotic behavior of the maximum likelihood estimators of...
AbstractIn the present paper, we study the asymptotic behavior for estimator of the drift parameter ...
to appear in Theory of Probability and its ApplicationsThis paper addresses the problem of estimatin...
We investigate the asymptotic behavior of the maximum likelihood estimators of the unknown parameter...
Our goal is to establish large deviations and concentration inequalities for the maximum likelihood ...
Abstract: Econometricians have recently been interested in estimating and testing the mean reversion...
Abstract. We investigate the asymptotic behavior of the maximum likelihood estimators of the unknown...
In this thesis, the extension of the Ornstein-Uhlenbeck process is studied by first driving this mo...
We study the problem of parameter estimation for generalized Ornstein-Uhlenbeck processes with small...
International audienceWe investigate the asymptotic behavior of the maximum likelihood estimators of...
AbstractAn asymptotic analysis is presented for estimation in the three-parameter Ornstein-Uhlenbeck...
AbstractThe exact distribution of the maximum-likelihood estimator of the drift (damping) parameter ...
The Ornstein-Uhlenbeck process has countless practical applications most of which rely on having pre...
This paper considers parameter estimation in the Ornstein–Uhlenbeck process observed in the presence...
This paper considers parameter estimation in the Ornstein–Uhlenbeck process observed in the presence...
International audienceWe investigate the asymptotic behavior of the maximum likelihood estimators of...
AbstractIn the present paper, we study the asymptotic behavior for estimator of the drift parameter ...
to appear in Theory of Probability and its ApplicationsThis paper addresses the problem of estimatin...
We investigate the asymptotic behavior of the maximum likelihood estimators of the unknown parameter...
Our goal is to establish large deviations and concentration inequalities for the maximum likelihood ...
Abstract: Econometricians have recently been interested in estimating and testing the mean reversion...
Abstract. We investigate the asymptotic behavior of the maximum likelihood estimators of the unknown...
In this thesis, the extension of the Ornstein-Uhlenbeck process is studied by first driving this mo...
We study the problem of parameter estimation for generalized Ornstein-Uhlenbeck processes with small...
International audienceWe investigate the asymptotic behavior of the maximum likelihood estimators of...