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pro vyhledávání: '"Birgé, Lucien"'
Autor:
Baraud, Yannick, Birgé, Lucien
We consider the problem of estimating the joint distribution $P$ of $n$ independent random variables within the Bayes paradigm from a non-asymptotic point of view. Assuming that $P$ admits some density $s$ with respect to a given reference measure, w
Externí odkaz:
http://arxiv.org/abs/1711.08328
Autor:
Baraud, Yannick, Birgé, Lucien
This paper is based on our personal notes for the short course we gave on January 5, 2017 at Institut Henri Poincar\'e, after an invitation of the SFdS. Our purpose is to give an overview of the method of $\rho$-estimation and of the optimality and r
Externí odkaz:
http://arxiv.org/abs/1703.01654
Autor:
Baraud, Yannick, Birgé, Lucien
Publikováno v:
The Annals of Statistics, 2020 Dec 01. 48(6), 3699-3720.
Externí odkaz:
https://www.jstor.org/stable/27028760
Autor:
Baraud, Yannick, Birgé, Lucien
Following Baraud, Birg\'e and Sart (2017), we pursue our attempt to design a robust universal estimator of the joint ditribution of $n$ independent (but not necessarily i.i.d.) observations for an Hellinger-type loss. Given such observations with an
Externí odkaz:
http://arxiv.org/abs/1605.05051
We define a general V-fold cross-validation type method based on robust tests, which is an extension of the hold-out defined by Birg{\'e} [7, Section 9]. We give some theoretical results showing that, under some weak assumptions on the considered sta
Externí odkaz:
http://arxiv.org/abs/1506.04692
Autor:
Baraud, Yannick, Birgé, Lucien
Publikováno v:
Stochastic Process. Appl., 126 (12):3888--3912 (2016)
The purpose of this paper is to pursue our study of rho-estimators built from i.i.d. observations that we defined in Baraud et al. (2014). For a \rho-estimator based on some model S (which means that the estimator belongs to S) and a true distributio
Externí odkaz:
http://arxiv.org/abs/1503.04427
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:
Birgé, Lucien
This paper investigates the {\em nonasymptotic} properties of Bayes procedures for estimating an unknown distribution from $n$ i.i.d.\ observations. We assume that the prior is supported by a model $(\scr{S},h)$ (where $h$ denotes the Hellinger dista
Externí odkaz:
http://arxiv.org/abs/1402.3695
Autor:
Baraud, Yannick, Birgé, Lucien
We consider the problem of estimating a function $s$ on $[-1,1]^{k}$ for large values of $k$ by looking for some best approximation by composite functions of the form $g\circ u$. Our solution is based on model selection and leads to a very general ap
Externí odkaz:
http://arxiv.org/abs/1102.2818
Autor:
Birgé, Lucien
We consider here estimation of an unknown probability density s belonging to L2(mu) where mu is a probability measure. We have at hand n i.i.d. observations with density s and use the squared L2-norm as our loss function. The purpose of this paper is
Externí odkaz:
http://arxiv.org/abs/0808.1416