Matthieu Lerasle

Optimal model selection in density estimation

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

Sub-Gaussian mean estimators

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

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

Optimal model selection for stationary data under various mixing conditions

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.

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

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

An oracle approach for interaction neighborhood estimation in random fields

beta

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

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Optimal Kernel Selection for Density Estimation

tau

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

-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.

Family-Wise Separation Rates for multiple testing

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