Mathieu Sart
Centre national de la recherche scientifique
13 Papers
54 Citations
Mathieu Sart is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Estimator & Hellinger distance. The author has an hindex of 6, co-authored 11 publications.
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Papers
A new method for estimation and model selection:\rho -estimation
TL;DR: In this article, the authors present a new estimation procedure that can be applied in various statistical frameworks including density and regression and which leads to both robust and optimal (or nearly optimal) estimators, which asymptotically coincide with the celebrated maximum likelihood estimators at least when the statistical model is regular enough and contains the true density to estimate.
A new method for estimation and model selection: $\rho$-estimation
TL;DR: In this paper, the authors 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, which asymptotically coincide with the celebrated maximum likelihood estimators at least when the statistical model is regular enough and contains the true density to estimate.
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Estimating the conditional density by histogram type estimators and model selection
TL;DR: In this paper, a new estimation procedure of the conditional density for independent and identically distributed data is proposed, which aims at using the data to select a function among arbitrary (at most countable) collections of candidates.
Estimation of the transition density of a Markov chain
TL;DR: In this paper, deux procedures for estimating the densite de transition of a chaine de Markov homogene are presented, the premiere procedure constrains un estimateur constant par morceaux sur a partition aleatoire bien choisie.
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•Posted Content
Estimating the conditional density by histogram type estimators and model selection
TL;DR: An adaptive piecewise constant estimator is derived on a random partition that achieves the expected rate of convergence over (possibly inhomogeneous and anisotropic) Besov spaces of small regularity and guarantees the existence of estimators possessing nice statistical properties under various assumptions on the conditional density.