Matthieu Lerasle

Learn 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 -fold(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 values(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 -fold(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 loss of the selected estimator. We first prove a non-asymptotic oracle inequality for V -fold cross-validation and its bias-corrected version (V -fold(More)
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-g(More)