Antoine Chambaz

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This paper deals with order identification for nested models in the i.i.d. framework. We study the asymptotic efficiency of two generalized likelihood ratio tests of the order. They are based on two estimators which are proved to be strongly consistent. A version of Stein's lemma yields an optimal underestimation error exponent. The lemma also implies that(More)
Neural integrators and working memory rely on persistent activity, a widespread neural phenomenon potentially involving persistent sodium conductances. Using a unique combination of voltage-clamp, dynamic-clamp, and frequency-domain techniques, we have investigated the role of voltage-dependent conductances on the dendritic electrotonic structure of neurons(More)
This paper deals with order identification for Markov chains with Markov regime (MCMR) in the context of finite alphabets. We define the joint order of a MCMR process in terms of the number k of states of the hidden Markov chain and the memory m of the conditional Markov chain. We study the properties of penalized maximum likelihood estimators for the(More)
The most important knowledge in the area of biology currently consists of raw text documents. Bibliographic databases of biomedical articles can be searched, but an efficient procedure should evaluate the relevance of documents to biology. In genetics, this challenge is even trickier, because of the lack of consistency in genes' naming tradition. We aim to(More)
We define a new measure of variable importance of an exposure on a continuous outcome, accounting for potential confounders. The exposure features a reference level x(0) with positive mass and a continuum of other levels. For the purpose of estimating it, we fully develop the semi-parametric estimation methodology called targeted minimum loss estimation(More)
We study a variant of the multi-armed bandit problem with multiple plays in which the user wishes to sample the m out of k arms with the highest expected rewards, but at any given time can only sample ≤ m arms. When = m, Thompson sampling was recently shown to be asymptotically efficient. We derive an asymptotic regret lower bound for any uniformly(More)
The efficiency of two Bayesian order estimators is studied under weak assumptions. By using nonparametric techniques, we prove new nonasymptotic underestimation and overestimation bounds. The bounds compare favorably with optimal bounds yielded by the Stein lemma and also with other known asymptotic bounds. The results apply to mixture models. In this case,(More)
UNLABELLED We describe the implementation of the method introduced by Chambaz et al. in 2012. We also demonstrate its genome-wide application to the integrative search of new regions with strong association between DNA copy number and gene expression accounting for DNA methylation in breast cancers. AVAILABILITY AND IMPLEMENTATION An open-source R package(More)