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Dive into the research topics where Christophe Giraud is active.

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Featured researches published by Christophe Giraud.


Annals of Statistics | 2009

Gaussian model selection with an unknown variance

Yannick Baraud; Christophe Giraud; Sylvie Huet

Let Y be a Gaussian vector whose components are independent with a common unknown variance. We consider the problem of estimating the mean μ of Y by model selection. More precisely, we start with a collection


Statistical Science | 2012

High-dimensional regression with unknown variance

Christophe Giraud; Sylvie Huet; Nicolas Verzelen

\mathcal{S}=\{S_{m},m\in\mathcal{M}\}


Electronic Journal of Statistics | 2011

Low rank multivariate regression

Christophe Giraud

of linear subspaces of ℝn and associate to each of these the least-squares estimator of μ on Sm. Then, we use a data driven penalized criterion in order to select one estimator among these. Our first objective is to analyze the performance of estimators associated to classical criteria such as FPE, AIC, BIC and AMDL. Our second objective is to propose better penalties that are versatile enough to take into account both the complexity of the collection


Molecular Biology and Evolution | 2013

Yeast Proteome Variations Reveal Different Adaptive Responses to Grape Must Fermentation

Mélisande Blein-Nicolas; Warren Albertin; Benoı̂t Valot; Philippe Marullo; Delphine Sicard; Christophe Giraud; Sylvie Huet; Aurélie Bourgais; Christine Dillmann; Dominique de Vienne; Michel Zivy

\mathcal{S}


PLOS ONE | 2015

Hybridization within Saccharomyces Genus Results in Homoeostasis and Phenotypic Novelty in Winemaking Conditions

Telma da Silva; Warren Albertin; Christine Dillmann; Marina Bely; Stéphane la Guerche; Christophe Giraud; Sylvie Huet; Delphine Sicard; Isabelle Masneuf-Pomarède; Dominique de Vienne; Philippe Marullo

and the sample size. Then we apply those to solve various statistical problems such as variable selection, change point detections and signal estimation among others. Our results are based on a nonasymptotic risk bound with respect to the Euclidean loss for the selected estimator. Some analogous results are also established for the Kullback loss.


Electronic Journal of Statistics | 2008

Estimation of Gaussian graphs by model selection

Christophe Giraud

We review recent results for high-dimensional sparse linear re- gression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity and variation- sparsity. The emphasis is put on nonasymptotic analyses and feasible pro- cedures. In addition, a small numerical study compares the practical perfor- mance of three schemes for tuning the lasso estimator and some references are collected for some more general models, including multivariate regres- sion and nonparametric regression.


Annales De L Institut Henri Poincare-probabilites Et Statistiques | 2014

Estimator selection in the Gaussian setting

Yannick Baraud; Christophe Giraud; Sylvie Huet

We consider in this paper the multivariate regression problem, when the target regression matrix


Statistical Applications in Genetics and Molecular Biology | 2012

Graph selection with GGMselect.

Christophe Giraud; Sylvie Huet; Nicolas Verzelen

A


Proteomics | 2012

Including shared peptides for estimating protein abundances: A significant improvement for quantitative proteomics

Mélisande Blein-Nicolas; Hao Xu; Dominique de Vienne; Christophe Giraud; Sylvie Huet; Michel Zivy

is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non-asymptotic oracle inequality for the resulting estimator. We also investigate the easier case where the variance of the noise is known and outline that the penalties appearing in our criterions are minimal (in some sense). These penalties involve the expected value of the Ky-Fan quasi-norm of some random matrices. These quantities can be evaluated easily in practice and upper-bounds can be derived from recent results in random matrix theory.


Annals of Statistics | 2012

DISCUSSION OF "LATENT VARIABLE GRAPHICAL MODEL SELECTION VIA CONVEX OPTIMIZATION"

Christophe Giraud; Alexandre B. Tsybakov

Saccharomyces cerevisiae and S. uvarum are two domesticated species of the Saccharomyces sensu stricto clade that diverged around 100 Ma after whole-genome duplication. Both have retained many duplicated genes associated with glucose fermentation and are characterized by the ability to achieve grape must fermentation. Nevertheless, these two species differ for many other traits, indicating that they underwent different evolutionary histories. To determine how the evolutionary histories of S. cerevisiae and S. uvarum are mirrored on the proteome, we analyzed the genetic variability of the proteomes of domesticated strains of these two species by quantitative mass spectrometry. Overall, 445 proteins were quantified. Massive variations of protein abundances were found, that clearly differentiated the two species. Abundance variations in specific metabolic pathways could be related to phenotypic traits known to discriminate the two species. In addition, proteins encoded by duplicated genes were shown to be differently recruited in each species. Comparing the strain differentiation based on the proteome variability to those based on the phenotypic and genetic variations further revealed that the strains of S. uvarum and some strains of S. cerevisiae displayed similar fermentative performances despite strong proteomic and genomic differences. Altogether, these results indicate that the ability of S. cerevisae and S. uvarum to complete grape must fermentation arose through different evolutionary roads, involving different metabolic pathways and duplicated genes.

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Sylvie Huet

Institut national de la recherche agronomique

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Romain Julliard

Centre national de la recherche scientifique

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Nicolas Verzelen

Institut national de la recherche agronomique

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Camille Coron

Université Paris-Saclay

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Mélisande Blein-Nicolas

Institut national de la recherche agronomique

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