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- Matthieu Lerasle
- 2009

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

- Matthieu Lerasle
- 2009

We propose an algorithm to estimate the common density s of a stationary process X1, ..., Xn. We suppose that the process is either Î² or Ï„ -mixing. We provide a model selection procedure based on a generalization of Mallowsâ€™ Cp and we prove oracle inequalities for the selected estimator under a few prior assumptions on the collection of models and on theâ€¦ (More)

This paper studies V -fold cross-validation for model selection in least-squares density estimation. The goal is to provide theoretical grounds for choosing V in order to minimize the least-squares risk of the selected estimator. We first prove a non asymptotic oracle inequality for V -fold cross-validation and its bias-corrected version (V -foldâ€¦ (More)

- Matthieu Lerasle
- 2009

The history of statistical model selection goes back at least to Akaike [Aka70], [Aka73] and Mallows [Mal73]. They proposed to select among a collection of parametric models the one which minimizes an empirical loss plus some penalty term proportional to the dimension of the models. BirgÃ© & Massart [BM97] and Barron, BirgÃ© & Massart [BBM99] generalize thisâ€¦ (More)

Considering either two independent i.i.d. samples, or two independent samples generated from a heteroscedastic regression model, or two independent Poisson processes, we address the question of testing equality of their respective distributions. We first propose single testing procedures based on a general symmetric kernel. The corresponding critical valuesâ€¦ (More)

Abstract: This paper studies V -fold cross-validation for model selection in least-squares density estimation. The goal is to provide theoretical grounds for choosing V in order to minimize the least-squares loss of the selected estimator. We first prove a non asymptotic oracle inequality for V -fold crossvalidation and its bias-corrected version (V -foldâ€¦ (More)

- Sylvain Arlot, Matthieu Lerasle
- Journal of Machine Learning Research
- 2016

This paper studies V -fold cross-validation for model selection in least-squares density estimation. The goal is to provide theoretical grounds for choosing V in order to minimize the least-squares loss of the selected estimator. We first prove a non-asymptotic oracle inequality for V -fold cross-validation and its bias-corrected version (V -foldâ€¦ (More)

- Matthieu Lerasle
- 2017

- Matthieu Lerasle, Sandrine Guillou, +4 authors J-M MembrÃ©
- International journal of food microbiology
- 2014

The objective of this study was to develop a probabilistic model in order to determine the contamination level of Salmonella and Listeria monocytogenes in ready-to-cook poultry meat, after a high pressure (HP) treatment. The model included four steps: i) Reception of raw meat materials, mincing and mixing meat, ii) Partitioning and packaging into 200-gâ€¦ (More)