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Annales De L Institut Henri Poincare-probabilites Et Statistiques | 2012

Optimal model selection in density estimation

Matthieu Lerasle

We build penalized least-squares estimators using the slope heuristic and resampling penalties. We prove oracle inequalities for the selected estimator with leading constant asymptotically equal to


Annals of Statistics | 2016

Sub-Gaussian mean estimators

Luc Devroye; Matthieu Lerasle; Gábor Lugosi; Roberto Imbuzeiro Oliveira

1


Annals of Statistics | 2011

OPTIMAL MODEL SELECTION FOR DENSITY ESTIMATION OF STATIONARY DATA UNDER VARIOUS MIXING CONDITIONS

Matthieu Lerasle

. We compare the practical performances of these methods in a short simulation study.


Annals of Statistics | 2012

Optimal model selection for stationary data under various mixing conditions

Matthieu Lerasle

We discuss the possibilities and limitations of estimating the mean of a real-valued random variable from independent and identically distributed observations from a non-asymptotic point of view. In particular, we define estimators with a sub-Gaussian behavior even for certain heavy-tailed distributions. We also prove various impossibility results for mean estimators.


Bernoulli | 2016

Sharp oracle inequalities and slope heuristic for specification probabilities estimation in discrete random fields

Matthieu Lerasle; Daniel Yasumasa Takahashi

We propose a block-resampling penalization method for marginal density estimation with nonnecessary independent observations. When the data are


Electronic Journal of Statistics | 2011

An oracle approach for interaction neighborhood estimation in random fields

Matthieu Lerasle; Daniel Yasumasa Takahashi

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Annals of Applied Probability | 2017

The number of potential winners in Bradley–Terry model in random environment

Raphael Chetrite; Roland Diel; Matthieu Lerasle

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arXiv: Statistics Theory | 2016

Optimal Kernel Selection for Density Estimation

Matthieu Lerasle; Nelo Molter Magalhães; Patricia Reynaud-Bouret

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international conference on high performance computing and simulation | 2016

Parallel and pseudorandom discrete event system specification vs. networks of spiking neurons: Formalization and preliminary implementation results

Alexandre Muzy; Matthieu Lerasle; Franck Grammont; Van Toan Dao; David R. C. Hill

-mixing, the selected estimator satisfies oracle inequalities with leading constant asymptotically equal to 1. We also prove in this setting the slope heuristic, which is a data-driven method to optimize the leading constant in the penalty.


Annals of Statistics | 2016

Family-Wise Separation Rates for multiple testing

Magalie Fromont; Matthieu Lerasle; Patricia Reynaud-Bouret

We build penalized least-squares estimators of the marginal density of a stationary process, using the slope algorithm and resampling penalties. When the data are

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