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pro vyhledávání: '"Sart, Mathieu"'
Autor:
Sart, Mathieu
Cette thèse porte sur l'estimation de fonctions à l'aide de tests dans trois cadres statistiques différents. Nous commençons par étudier le problème de l'estimation des intensités de processus de Poisson avec covariables. Nous démontrons un t
Externí odkaz:
http://www.theses.fr/2013NICE4097/document
Autor:
Sart, Mathieu
We propose a new estimation procedure of the conditional density for independent and identically distributed data. Our procedure aims at using the data to select a function among arbitrary (at most countable) collections of candidates. By using a det
Externí odkaz:
http://arxiv.org/abs/1512.07052
Publikováno v:
Invent. math. vol. 207, 425-517 (2017)
The aim of this paper is to present a new estimation procedure that can be applied in many statistical frameworks including density and regression and which leads to both robust and optimal (or nearly optimal) estimators. In density estimation, they
Externí odkaz:
http://arxiv.org/abs/1403.6057
Autor:
Sart, Mathieu
Publikováno v:
Bernoulli 2016, Vol. 22, No. 3, 1617-1670
We are interested in the problem of robust parametric estimation of a density from $n$ i.i.d. observations. By using a practice-oriented procedure based on robust tests, we build an estimator for which we establish non-asymptotic risk bounds with res
Externí odkaz:
http://arxiv.org/abs/1308.2927
Autor:
Sart, Mathieu
We present two data-driven procedures to estimate the transition density of an homogeneous Markov chain. The first yields to a piecewise constant estimator on a suitable random partition. By using an Hellinger-type loss, we establish non-asymptotic r
Externí odkaz:
http://arxiv.org/abs/1210.5165
Autor:
Sart, Mathieu
We observe $n$ inhomogeneous Poisson processes with covariates and aim at estimating their intensities. We assume that the intensity of each Poisson process is of the form $s (\cdot, x)$ where $x$ is the covariate and where $s$ is an unknown function
Externí odkaz:
http://arxiv.org/abs/1112.5634
Akademický článek
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Autor:
Sart, Mathieu
Publikováno v:
Bernoulli. 29
In this paper, we carry out a piecewise constant estimator of the density for privatised data. We establish a non-asymptotic oracle inequality for the Hellinger loss and deduce that our estimator is adaptive and (almost) rate optimal over a wide rang
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bffa520bde8a73d5000a22c75264892b
https://doi.org/10.3150/22-bej1543
https://doi.org/10.3150/22-bej1543
Autor:
SART, MATHIEU
Publikováno v:
Bernoulli, 2016 Aug 01. 22(3), 1617-1670.
Externí odkaz:
http://www.jstor.org/stable/43864121
Autor:
Sart, Mathieu
We investigate the problem of density estimation on the real line $\mathbb{R}$ under $\mathbb{L}^1$ loss. We carry out a new way to select the important coefficients in some wavelet expansions. We study the resulting estimator when the density is smo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______166::4a19ef390401ff2fc87fa8e7c81ff34d
https://hal.science/hal-03915562
https://hal.science/hal-03915562