AbstractIn this paper, we state a large deviation principle (LDP) and sharp LDP for maximum likelihood estimators of drift coefficients of generalized squared radial Ornstein–Uhlenbeck processes. For that purpose, we present an LDP in a class of non-steep cases, where the Gärtner–Ellis theorem cannot be applied
We investigate the probabilities of large deviations of the maximum likelihood estimator and Bayes e...
We consider a class of diffusion processes on Euclidean spaces, with the drift terms not weaker than...
Abstract. We consider a class of diffusion processes on Euclidean spaces, with the drift terms not w...
We establish large deviation principles for the couple of the maximum likelihood estimators of dimen...
AbstractIn this paper, we state a large deviation principle (LDP) and sharp LDP for maximum likeliho...
A large deviation principle (LDP) with an explicit rate function is proved for the estimation of dri...
We investigate the large deviation properties of the maximum likelihood estimators for the Ornstein-...
Abstract. We investigate the large deviation properties of the maximum likeli-hood estimators for th...
We investigate the large deviation properties of the maximum likelihood estimators for the Ornstein-...
AbstractFor the Ornstein–Uhlenbeck process, the asymptotic behavior of the maximum likelihood estima...
Our goal is to establish large deviations and concentration inequalities for the maximum likelihood ...
For the Ornstein-Uhlenbeck process, the asymptotic behavior of the maximum likelihood estimator of t...
For the Ornstein-Uhlenbeck process, the asymptotic behavior of the maximum likelihood estimator of t...
International audienceFor the Ornstein-Uhlenbeck process, the asymptotic behavior of the maximum lik...
We study moderate deviations for maximum likelihood estimators of parameters in generalized squared ...
We investigate the probabilities of large deviations of the maximum likelihood estimator and Bayes e...
We consider a class of diffusion processes on Euclidean spaces, with the drift terms not weaker than...
Abstract. We consider a class of diffusion processes on Euclidean spaces, with the drift terms not w...
We establish large deviation principles for the couple of the maximum likelihood estimators of dimen...
AbstractIn this paper, we state a large deviation principle (LDP) and sharp LDP for maximum likeliho...
A large deviation principle (LDP) with an explicit rate function is proved for the estimation of dri...
We investigate the large deviation properties of the maximum likelihood estimators for the Ornstein-...
Abstract. We investigate the large deviation properties of the maximum likeli-hood estimators for th...
We investigate the large deviation properties of the maximum likelihood estimators for the Ornstein-...
AbstractFor the Ornstein–Uhlenbeck process, the asymptotic behavior of the maximum likelihood estima...
Our goal is to establish large deviations and concentration inequalities for the maximum likelihood ...
For the Ornstein-Uhlenbeck process, the asymptotic behavior of the maximum likelihood estimator of t...
For the Ornstein-Uhlenbeck process, the asymptotic behavior of the maximum likelihood estimator of t...
International audienceFor the Ornstein-Uhlenbeck process, the asymptotic behavior of the maximum lik...
We study moderate deviations for maximum likelihood estimators of parameters in generalized squared ...
We investigate the probabilities of large deviations of the maximum likelihood estimator and Bayes e...
We consider a class of diffusion processes on Euclidean spaces, with the drift terms not weaker than...
Abstract. We consider a class of diffusion processes on Euclidean spaces, with the drift terms not w...
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