Andrew Rukhin (1993)
Applicationes Mathematicae
Similarity:
The statistical estimation problem of the normal distribution function and of the density at a point is considered. The traditional unbiased estimators are shown to have Bayes nature and admissibility of related generalized Bayes procedures is proved. Also inadmissibility of the unbiased density estimator is demonstrated.
Cristina Butucea, Marie-Luce Taupin (2008)
Annales de l'I.H.P. Probabilités et statistiques
Similarity:
In the regression model with errors in variables, we observe i.i.d. copies of (, ) satisfying = ()+ and =+ involving independent and unobserved random variables , , plus a regression function , known up to a finite dimensional . The common densities of the ’s and of the ’s are unknown, whereas the distribution of is completely known. We aim at estimating the parameter by using the observations...
Asunción Rubio, Jan Amos Vísek (1997)
Qüestiió
Similarity:
An estimator of the contamination level of data is proposed in the framework of linear models and its asymptotic behavior is investigated. A numerical study illustrates its finite sample performance under an alternative.
A. Goldenshluger, A. Juditsky, A. Tsybakov, A. Zeevi (2008)
Annales de l'I.H.P. Probabilités et statistiques
Similarity:
We focus on the problem of adaptive estimation of signal singularities from indirect and noisy observations. A typical example of such a singularity is a discontinuity (change-point) of the signal or of its derivative. We develop a change-point estimator which adapts to the unknown smoothness of a nuisance deterministic component and to an unknown jump amplitude. We show that the proposed estimator attains optimal adaptive rates of convergence. A simulation study demonstrates reasonable...
María Del Carmen Pardo (1997)
Kybernetika
Similarity:
F. Comte, S. Gaïffas, A. Guilloux (2011)
Annales de l'I.H.P. Probabilités et statistiques
Similarity:
We propose in this work an original estimator of the conditional intensity of a marker-dependent counting process, that is, a counting process with covariates. We use model selection methods and provide a nonasymptotic bound for the risk of our estimator on a compact set. We show that our estimator reaches automatically a convergence rate over a functional class with a given (unknown) anisotropic regularity. Then, we prove a lower bound which establishes that this rate is optimal. Lastly,...