Search

Your search keyword '"Lavielle, Marc"' showing total 307 results
Start Over Author "Lavielle, Marc"
307 results on '"Lavielle, Marc"'

1. On the Global Convergence of (Fast) Incremental Expectation Maximization Methods

Author

Karimi, Belhal, Wai, Hoi-To, Moulines, Eric, and Lavielle, Marc

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology
Abstract

The EM algorithm is one of the most popular algorithm for inference in latent data models. The original formulation of the EM algorithm does not scale to large data set, because the whole data set is required at each iteration of the algorithm. To alleviate this problem, Neal and Hinton have proposed an incremental version of the EM (iEM) in which at each iteration the conditional expectation of the latent data (E-step) is updated only for a mini-batch of observations. Another approach has been proposed by Capp\'e and Moulines in which the E-step is replaced by a stochastic approximation step, closely related to stochastic gradient. In this paper, we analyze incremental and stochastic version of the EM algorithm as well as the variance reduced-version of Chen et. al. in a common unifying framework. We also introduce a new version incremental version, inspired by the SAGA algorithm by Defazio et. al. We establish non-asymptotic convergence bounds for global convergence. Numerical applications are presented in this article to illustrate our findings., Comment: 25 pages, Accepted at NeurIPS 2019

Published
2019

2. f-SAEM: A fast Stochastic Approximation of the EM algorithm for nonlinear mixed effects models

Author

Karimi, Belhal, Lavielle, Marc, and Moulines, Eric

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

The ability to generate samples of the random effects from their conditional distributions is fundamental for inference in mixed effects models. Random walk Metropolis is widely used to perform such sampling, but this method is known to converge slowly for medium dimensional problems, or when the joint structure of the distributions to sample is spatially heterogeneous. The main contribution consists of an independent Metropolis-Hastings (MH) algorithm based on a multidimensional Gaussian proposal that takes into account the joint conditional distribution of the random effects and does not require any tuning. Indeed, this distribution is automatically obtained thanks to a Laplace approximation of the incomplete data model. Such approximation is shown to be equivalent to linearizing the structural model in the case of continuous data. Numerical experiments based on simulated and real data illustrate the performance of the proposed methods. For fitting nonlinear mixed effects models, the suggested MH algorithm is efficiently combined with a stochastic approximation version of the EM algorithm for maximum likelihood estimation of the global parameters., Comment: Accepted in CSDA 2020, 35 pages. arXiv admin note: text overlap with arXiv:1910.12090

Published
2019

3. Efficient Metropolis-Hastings Sampling for Nonlinear Mixed Effects Models

Author

Karimi, Belhal and Lavielle, Marc

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

The ability to generate samples of the random effects from their conditional distributions is fundamental for inference in mixed effects models. Random walk Metropolis is widely used to conduct such sampling, but such a method can converge slowly for medium dimension problems, or when the joint structure of the distributions to sample is complex. We propose a Metropolis Hastings (MH) algorithm based on a multidimensional Gaussian proposal that takes into account the joint conditional distribution of the random effects and does not require any tuning, in contrast with more sophisticated samplers such as the Metropolis Adjusted Langevin Algorithm or the No-U-Turn Sampler that involve costly tuning runs or intensive computation. Indeed, this distribution is automatically obtained thanks to a Laplace approximation of the original model. We show that such approximation is equivalent to linearizing the model in the case of continuous data. Numerical experiments based on real data highlight the very good performances of the proposed method for continuous data model.

Published
2019

5. Logistic Regression with Missing Covariates -- Parameter Estimation, Model Selection and Prediction within a Joint-Modeling Framework

Author

Jiang, Wei, Josse, Julie, Lavielle, Marc, and Group, TraumaBase

Subjects
Statistics - Methodology
Abstract

Logistic regression is a common classification method in supervised learning. Surprisingly, there are very few solutions for performing logistic regression with missing values in the covariates. We suggest a complete approach based on a stochastic approximation version of the EM algorithm to do statistical inference with missing values including the estimation of the parameters and their variance, derivation of confidence intervals and a model selection procedure. We also tackle the problem of prediction for new observations (on a test set) with missing covariate data. The methodology is computationally efficient, and its good coverage and variable selection properties are demonstrated in a simulation study where we contrast its performances to other methods. For instance, the popular approach of multiple imputation by chained equations can lead to estimates that exhibit meaningfully greater biases than the proposed approach. We then illustrate the method on a dataset of severely traumatized patients from Paris hospitals to predict the occurrence of hemorrhagic shock, a leading cause of early preventable death in severe trauma cases. The aim is to consolidate the current red flag procedure, a binary alert identifying patients with a high risk of severe hemorrhage. The methodology is implemented in the R package misaem., Comment: R package misaem https://CRAN.R-project.org/package=misaem, R implementations https://github.com/wjiang94/miSAEM_logReg

Published
2018

9. Random threshold for linear model selection, revisited

Author

Keller, Merlin and Lavielle, Marc

Subjects
Statistics - Methodology
Abstract

In [Lavielle and Ludena 07], a random thresholding metho d is intro duced to select the significant, or non null, mean terms among a collection of independent random variables, and applied to the problem of recovering the significant coefficients in non ordered model selection. We intro duce a simple modification which removes the dep endency of the proposed estimator on a window parameter while maintaining its asymptotic properties. A simulation study suggests that both procedures compare favorably to standard thresholding approaches, such as multiple testing or model-based clustering, in terms of the binary classification risk. An application of the method to the problem of activation detection on functional magnetic resonance imaging (fMRI) data is discussed., Comment: 22 pages, 7 figures. Submitted to Statistics and its Interface (SII)

Published
2010

10. Selection of a Model of Cerebral Activity for fMRI Group Data Analysis

Author

Keller, Merlin, Roche, Alexis, and Lavielle, Marc

Subjects
Statistics - Applications, Mathematics - Statistics Theory
Abstract

This thesis is dedicated to the statistical analysis of multi-sub ject fMRI data, with the purpose of identifying bain structures involved in certain cognitive or sensori-motor tasks, in a reproducible way across sub jects. To overcome certain limitations of standard voxel-based testing methods, as implemented in the Statistical Parametric Mapping (SPM) software, we introduce a Bayesian model selection approach to this problem, meaning that the most probable model of cerebral activity given the data is selected from a pre-defined collection of possible models. Based on a parcellation of the brain volume into functionally homogeneous regions, each model corresponds to a partition of the regions into those involved in the task under study and those inactive. This allows to incorporate prior information, and avoids the dependence of the SPM-like approach on an arbitrary threshold, called the cluster- forming threshold, to define active regions. By controlling a Bayesian risk, our approach balances false positive and false negative risk control. Furthermore, it is based on a generative model that accounts for the spatial uncertainty on the localization of individual effects, due to spatial normalization errors. On both simulated and real fMRI datasets, we show that this new paradigm corrects several biases of the SPM-like approach, which either swells or misses the different active regions, depending on the choice of a cluster-forming threshold., Comment: PhD Thesis, 208 pages, Applied Statistics and Neuroimaging, University of Orsay, France

Published
2010

18. Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis

Author

Keller, Merlin, Lavielle, Marc, Perrot, Matthieu, Roche, Alexis, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Guang-Zhong, editor, Hawkes, David, editor, Rueckert, Daniel, editor, Noble, Alison, editor, and Taylor, Chris, editor

Published
2009
Full Text
View/download PDF

21. Using hospital data for monitoring the dynamics of COVID 19 in France

Author

Lavielle, Marc, Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Lavielle, Marc

Subjects
[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], COVID 19 data, statistical model, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, sense organs, change-points detection, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], ODE model
Abstract

International audience; The aim of this article is to show how daily hospital data can be used to track the evolution of the COVID-19 epidemic in France. A piecewise defined dynamic model allows a very good fit of the available data on hospital admissions, deathsand discharges. The change-points detected correspond to moments when the dynamics of the epidemic changed abruptly. Although the proposed model is relatively simple, it can serve several purposes: It is an analytical tool to betterunderstand what has happened so far by relating observed changes to changes in health policy or the evolution of the virus. It is also a surveillance tool that can be used effectively to warn of a resurgence of epidemic activity, and finally a short-term forecasting tool if conditions remain unchanged. The model, data and fits are implemented in an interactive web application.

Published
2021

26. Some EM-type algorithms for incomplete data model building

Author

Lavielle, Marc, Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Lavielle, Marc

Subjects
EM, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], SAEM, Penalized likelihood, Model selection, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
Abstract

We propose an extension of the EM algorithm and its stochastic versions for the construction of incomplete data models when the selected model minimizes a penalized likelihood criterion. This optimization problem is particularly challenging in the context of incomplete data, even when the model is relatively simple. However, by completing the data, the E-step of the algorithm allows us to simplify this problem of complete model selection into a classical problem of complete model selection that does not pose any major difficulties. We then show that the criterion to be minimized decreases with each iteration of the algorithm. Examples of the use of these algorithms are presented for the identification of regression mixture models and the construction of nonlinear mixed-effects models.

Published
2021

27. Synthèse sur la détection des produits issus des nouvelles technologies génomiques (NGT) appliquées aux plantes. Paris, Paris, le 26 novembre 2021

Author

Comité Scientifique Du Haut Conseil Des Biotechnologies, ., Angevin, Frédérique, Bagnis, Claude, Bar-Hen, Avner, Barny, Marie-Anne, Boireau, Pascal, Brévault, Thierry, Chauvel, Bruno, Couvet, Denis, Dassa, Elie, de Verneuil, Hubert, Demeneix, Barbara, Franche, Claudine, Guerche, Philippe, Guillemain, Joël, Hernandez Raquet, Guillermina, Khalife, Jamal, Klonjkowski, Bernard, Lavielle, Marc, Le Corre, Valérie, Lefèvre, François, Lemaire, Olivier, Lereclus, Didier, Maximilien, Rémy, Meurs, Eliane, Naffakh, Nadia, Négre, Didier, Ochatt, Sergio, Pages, Jean-Christophe, Raynaud, Xavier, Regnault-Roger, Catherine, Renard, Michel, Renault, Tristan, Saindrenan, Patrick, Simonet, Pascal, Troadec, Marie-Bérengère, Vaissière, Bernard, Vilotte, Jean-Luc, Haut Conseil des Biotechnologies (HCB), Unité Impacts Ecologiques des Innovations en Production Végétale (ECO-INNOV), Institut National de la Recherche Agronomique (INRA), Etablissement Français du Sang, Université Paris-Sud - Paris 11 (UP11), Université Paris Descartes - Paris 5 (UPD5), Institut d'écologie et des sciences de l'environnement de Paris (iEES Paris ), Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Direction des Ressources Humaines et du Développement Durable (DRHDD), Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Agroécologie [Dijon], Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Muséum national d'Histoire naturelle (MNHN), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Recherche pour le Développement (IRD), Institut Jean-Pierre Bourgin (IJPB), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agence nationale de sécurité du médicament et des produits de santé [Saint-Denis] (ANSM), Université Francois Rabelais [Tours], Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP), Virologie UMR1161 (VIRO), École nationale vétérinaire - Alfort (ENVA)-Institut National de la Recherche Agronomique (INRA)-Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Unité de recherche Virologie et Immunologie Moléculaires (VIM (UR 0892)), Santé de la vigne et qualité du vin (SVQV), Institut National de la Recherche Agronomique (INRA)-Université de Strasbourg (UNISTRA), MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut Pasteur [Paris] (IP), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Institut des Sciences des Plantes de Paris-Saclay (IPS2 (UMR_9213 / UMR_1403)), Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-Université Paris Diderot - Paris 7 (UPD7)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Abeilles et environnement (AE), Génétique Animale et Biologie Intégrative (GABI), Haut Conseil des Biotechnologies, Commanditaire : Ministère de l'Agriculture, de l'Agroalimentaire et de la Forêt (France), Type de commande : Commande avec contrat/convention/lettre de saisine, Type de commanditaire ou d'auteur de la saisine : Ministères, parlements et les structures qui leur sont directement rattachées, Institut d'écologie et des sciences de l'environnement de Paris (iEES), Institut National de la Recherche Agronomique (INRA)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Massart, Isabelle, Institut Pasteur [Paris], Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest

Subjects
Plant Breeding, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, New Genomic Technics, [SDV.BIO] Life Sciences [q-bio]/Biotechnology
Abstract

Synthèse sur la détection des produits issus des nouvelles technologies génomiques (NGT) appliquées aux plantesLa question du statut réglementaire des NGT (New Genomic Techniques), qui ont été définies comme techniques de modification du génome « apparues » depuis 2001 , est actuellement débattue en Europe. Ces techniques n’existaient pas, ou étaient peu développées lors de l’adoption des directives et règlements européens qui encadrent actuellement les organismes génétiquement modifiés (OGM). A l’époque le législateur européen avait choisi de réglementer les techniques de transgénèse et d’exempter les méthodes de mutagénèse notamment sur la base d’une expérience d’usage n’ayant révélé aucun problème particulier. Certaines NGT pouvant être utilisées pour induire une mutagenèse, ciblée ou non , la questionde leur soumission à la réglementation européenne en vigueur fait l’objet de discussions juridiques et politiques depuis quelques années. Les données scientifiques relatives à ces technologies (définitions, méthodes et résultats) doivent permettre de guider ces discussions.

Published
2021

28. SAMBA: A novel method for fast automatic model building in nonlinear mixed-effects models

Author

Prague, Mélanie, Lavielle, Marc, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), This study has received funding from the Nipah virus project financed by the French Ministry of Higher Education, Research and Innovation., and Prague, Mélanie

Subjects
Nonlinear models, [STAT.ME] Statistics [stat]/Methodology [stat.ME], [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], Modeling, [MATH.MATH-DS] Mathematics [math]/Dynamical Systems [math.DS], RM1-950, Stochastic algorithm, Covariate model selection, Nonlinear Dynamics, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Population PKPD, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Research Design, Modeling and Simulation, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Humans, Pharmacology (medical), [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Therapeutics. Pharmacology, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], mixed-effects model, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Algorithms
Abstract

The success of correctly identifying all the components of a nonlinear mixed‐effects model is far from straightforward: it is a question of finding the best structural model, determining the type of relationship between covariates and individual parameters, detecting possible correlations between random effects, or also modeling residual errors. We present the Stochastic Approximation for Model Building Algorithm (SAMBA) procedure and show how this algorithm can be used to speed up this process of model building by identifying at each step how best to improve some of the model components. The principle of this algorithm basically consists in “learning something” about the “best model,” even when a “poor model” is used to fit the data. A comparison study of the SAMBA procedure with Stepwise Covariate Modeling (SCM) and COnditional Sampling use for Stepwise Approach (COSSAC) show similar performances on several real data examples but with a much reduced computing time. This algorithm is now implemented in Monolix and in the R package Rsmlx.

Published
2021

29. AVIS en réponse à la saisine HCB - dossier 2020-172. Paris, le 06 septembre 2021

Author

Comité Scientifique Du Haut Conseil Des Biotechnologies, ., Angevin, Frédérique, Bagnis, Claude, Bar-Hen, Avner, Barny, Marie-Anne, Boireau, Pascal, Brévault, Thierry, Chauvel, Bruno, Collonnier, Cécile, Couvet, Denis, Dassa, Elie, de Verneuil, Hubert, Franche, Claudine, Guerche, Philippe, Guillemain, Joël, Hernandez Raquet, Guillermina, Khalife, Jamal, Klonjkowski, Bernard, Lavielle, Marc, Le Corre, Valérie, Lefèvre, François, Lemaire, Olivier, Lereclus, Didier, Maximilien, Rémy, Meurs, Eliane, Naffakh, Nadia, Négre, Didier, Ochatt, Sergio, Pages, Jean-Christophe, Raynaud, Xavier, Regnault-Roger, Catherine, Renard, Michel, Renault, Tristan, Saindrenan, Patrick, Simonet, Pascal, Troadec, Marie-Bérengère, Vaissière, Bernard, Vilotte, Jean-Luc, Haut Conseil des Biotechnologies (HCB), Unité Impacts Ecologiques des Innovations en Production Végétale (ECO-INNOV), Institut National de la Recherche Agronomique (INRA), Etablissement Français du Sang, Université Paris-Sud - Paris 11 (UP11), Université Paris Descartes - Paris 5 (UPD5), Institut d'écologie et des sciences de l'environnement de Paris (iEES Paris ), Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Direction des Ressources Humaines et du Développement Durable (DRHDD), Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Agroécologie [Dijon], Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut Jean-Pierre Bourgin (IJPB), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Muséum national d'Histoire naturelle (MNHN), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Recherche pour le Développement (IRD), Agence nationale de sécurité du médicament et des produits de santé [Saint-Denis] (ANSM), Université Francois Rabelais [Tours], Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP), Virologie UMR1161 (VIRO), École nationale vétérinaire - Alfort (ENVA)-Institut National de la Recherche Agronomique (INRA)-Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Unité de recherche Virologie et Immunologie Moléculaires (VIM (UR 0892)), Santé de la vigne et qualité du vin (SVQV), Institut National de la Recherche Agronomique (INRA)-Université de Strasbourg (UNISTRA), MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut Pasteur [Paris] (IP), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Institut des Sciences des Plantes de Paris-Saclay (IPS2 (UMR_9213 / UMR_1403)), Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-Université Paris Diderot - Paris 7 (UPD7)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Abeilles et environnement (AE), Génétique Animale et Biologie Intégrative (GABI), Haut Conseil des Biotechnologies, Commanditaire : Ministère de l'Agriculture, de l'Agroalimentaire et de la Forêt (France), Type de commande : Commande avec contrat/convention/lettre de saisine, Type de commanditaire ou d'auteur de la saisine : Ministères, parlements et les structures qui leur sont directement rattachées, Massart, Isabelle, Institut d'écologie et des sciences de l'environnement de Paris (iEES), Institut National de la Recherche Agronomique (INRA)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris], Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest

Subjects
[SDV.BIO]Life Sciences [q-bio]/Biotechnology, [SDV.BIO] Life Sciences [q-bio]/Biotechnology
Abstract

Le Haut Conseil des biotechnologies (HCB) a été saisi le 22 juin 2021 par les autorités compétentes françaises (Ministère de l'Agriculture et de l'Alimentation) d'une demande d'avis relative au dossier EFSA-GMO-NL-2020-172 de demande d'autorisation de mise sur le marché du maïs génétiquement modifié DP915635 à des fins d'importation, transformation et alimentation humaine et animale. Ce dossier a été déposé par la société Pioneer Hi-Bred International, Inc. auprès des autorités compétentes néerlandaises sur le fondement du règlement (CE) n° 1829/2003. Dans le cadre de ce règlement, l'évaluation des dossiers de demande de mise sur le marché est confiée à l'Autorité européenne de sécurité des aliments (EFSA). Les Etats membres disposent de trois mois pour envoyer leurs commentaires à l'EFSA en contribution à l'évaluation du dossier. Dans ce contexte, le HCB est invité à proposer des commentaires à destination de l'EFSA au plus tard le 07 septembre 2021.

Published
2021

33. Genetic analysis of growth curves using the SAEM algorithm

Author

Lavielle Marc, Meza Cristian, Jaffrézic Florence, and Foulley Jean-Louis

Subjects
genetic analysis, growth curves, longitudinal data, stochastic approximation EM algorithm, Animal culture, SF1-1100, Genetics, QH426-470
Abstract

Abstract The analysis of nonlinear function-valued characters is very important in genetic studies, especially for growth traits of agricultural and laboratory species. Inference in nonlinear mixed effects models is, however, quite complex and is usually based on likelihood approximations or Bayesian methods. The aim of this paper was to present an efficient stochastic EM procedure, namely the SAEM algorithm, which is much faster to converge than the classical Monte Carlo EM algorithm and Bayesian estimation procedures, does not require specification of prior distributions and is quite robust to the choice of starting values. The key idea is to recycle the simulated values from one iteration to the next in the EM algorithm, which considerably accelerates the convergence. A simulation study is presented which confirms the advantages of this estimation procedure in the case of a genetic analysis. The SAEM algorithm was applied to real data sets on growth measurements in beef cattle and in chickens. The proposed estimation procedure, as the classical Monte Carlo EM algorithm, provides significance tests on the parameters and likelihood based model comparison criteria to compare the nonlinear models with other longitudinal methods.

Published
2006
Full Text
View/download PDF

34. SPIX: a new software package to reveal chemical reactions at trace amounts in very complex mixtures from high-resolution mass spectra data sets

Author

Nicol, Edith, Xu, Yao, Varga, Zsuzsanna, Kinani, Said, Bouchonnet, Stéphane, Lavielle, Marc, Laboratoire de chimie moléculaire (LCM), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X), Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire National d’Hydraulique et Environnement (EDF R&D LNHE), EDF R&D (EDF R&D), EDF (EDF)-EDF (EDF), Lavielle, Marc, and École polytechnique (X)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
High-resolution mass spectrometry, complex mixtures, kinetics, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [CHIM] Chemical Sciences, software development, spectral big data management, chemical reactions, [CHIM]Chemical Sciences, non-targeted approach, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
Abstract

International audience; Rationale: High-resolution mass spectrometry-based non-targeted screening has a huge potential for applications in environmental sciences, engineering and regulation. However, it produces big data for which full appropriate processing is a real challenge; the development of processing software is the last building-block to enable large-scale use of this approach. Methods: A new software application, SPIX, has been developed to extract relevant information from high-resolution mass-spectrum datasets. Dealing with intrinsic sample variability and reducing operator subjectivity, it opens up opportunities and promising prospects in many areas of analytical chemistry. SPIX is freely available at: http://spix.webpopix.org. Results: Two features of the software are presented in the field of environmental analysis. An example illustrates how SPIX reveals photodegradation reactions in wastewater by fitting kinetic models to significant changes in ion abundance over time. A second example shows the ability of SPIX to detect photoproducts at trace amounts in river water, through comparison of datasets from samples taken before and after irradiation. Conclusions: SPIX has shown its ability to reveal relevant modifications between two series of big data sets, allowing for instance to study the consequences of a given event on a complex substrate. Most of alland this is to our knowledge the only software currently available allowing thatit can reveal and monitor any kind of reaction in all types of mixture.

Published
2020

35. An ensemble model based on early predictors to forecast COVID-19 healthcare demand in France

Author

Paireau, Juliette, Andronico, Alessio, Hozé, Nathanaël, Layan, Maylis, Crepey, Pascal, Roumagnac, Alix, Lavielle, Marc, Boëlle, Pierre-Yves, Cauchemez, Simon, Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Direction des maladies infectieuses - Infectious Diseases Division [Saint-Maurice], Santé publique France - French National Public Health Agency [Saint-Maurice, France], Recherche en Pharmaco-épidémiologie et Recours aux Soins (REPERES), École des Hautes Études en Santé Publique [EHESP] (EHESP)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), École des Hautes Études en Santé Publique [EHESP] (EHESP), Département Méthodes quantitatives en santé publique (METIS), Predict Services [Castelnau-le-Lez], Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École des Hautes Études en Santé Publique [EHESP] (EHESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPC), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), and Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP)

Subjects
Hospitalization, COVID-19, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Prediction, Model, Hospital admissions
Abstract

Short-term forecasting of the COVID-19 pandemic is required to facilitate the planning of COVID-19 healthcare demand in hospitals. Here, we evaluate the performance of 12 individual models and 19 predictors to anticipate French COVID-19 related healthcare needs from September 7th 2020 to March 6th 2021. We then build an ensemble model by combining the individual forecasts and test this model from March 7th to July 6th 2021. We find that inclusion of early predictors (epidemiological, mobility and meteorological predictors) can halve the root mean square error for 14-day ahead forecasts, with epidemiological and mobility predictors contributing the most to the improvement. On average, the ensemble model is the best or second best model, depending on the evaluation metric. Our approach facilitates the comparison and benchmarking of competing models through their integration in a coherent analytical framework, ensuring avenues for future improvements can be identified.

Published
2021

37. Alternative subgroup joint analysis proposal of nonlinear longitudinal and time-to-event data for modeling pregnancy miscarriage

Author

de La Cruz, Rolando, Lavielle, Marc, Meza, Cristian, Núñez-Antón, Vicente, Universidad Adolfo Ibáñez [Santiago], Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centro de Investigación y Modelamiento de Fenómenos Aleatorios – Valparaíso (CIMFAV), Universidad de Valparaiso [Chile], University of the Basque Country [Bizkaia] (UPV/EHU), and University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU)

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], hormones, hormone substitutes, and hormone antagonists
Abstract

Pregnancies achieved through in-vitro fertilization (IVF) are associated with adverse firsttrimester outcomes in comparison to spontaneously achieved pregnancies. Human chorionicgonadotrophin β subunit (β-HCG) is a well-known and accurate biomarker for the diagnosis and monitoring of pregnancy after IVF. Low levels of β-HCG during the first trimesterof pregnancy are related to miscarriage, ectopic pregnancy and failure of IVF procedure.Longitudinal profiles of β-HCG can be utilized to distinguish between normal and abnormalpregnancies, and to assist and guide the clinician in better management and monitoring ofpost-IVF pregnancies. Therefore, being able to assess the association between longitudinallymeasured β-HCG and time to early miscarriage is of crucial interest to clinicians. A commonjoint modeling approach proposal to achieve this objective is to use subject-specific randomeffects in a mixed effects model for longitudinal β-HCG data as predictors in a model for thetime-to-event (TTE) data. This work was motivated by an observational study with normaland abnormal pregnancies where serum concentrations of β-HCG were measured in 173young women during a gestational age of 9-86 days at a private clinic in Santiago, Chile.Some women experienced a miscarriage event but the rest did not. For those women whoexperienced miscarriage, their exact event times were unknown, in such case we have interval censored data, assuming that the event occurred between the last time of the observedβ-HCG measurement and ten days after. For our dataset we consider a nonlinear mixedeffects (NLME) model for both normal and abnormal pregnancies, but the joint model isconsidered only for the subgroup of miscarriage women. All the estimation procedures arebased on the Stochastic Approximation of the EM (SAEM) algorithm implemented in theMonolix software.

Published
2020

38. CYFIP2 containing WAVE complexes inhibit cell migration

Author

Polesskaya, Anna, Boutillon, Arthur, Wang, Yanan, Lavielle, Marc, Vacher, Sophie, Schnitzler, Anne, Molinie, Nicolas, Rocques, Nathalie, Fokin, Artem, Bièche, Ivan, David, Nicolas, Gautreau, Alexis, Laboratoire de Biologie Structurale de la Cellule (BIOC), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Institut Curie [Paris]

Subjects
knock-out, Arp2/3 MFS, knock-down, Scar/WAVE, [SDV]Life Sciences [q-bio], metastasis-free survival, KD, multiprotein complexes, KO, Patient cohort, Zebrafish embryo, morpholino, MO
Abstract

Branched actin networks polymerized by the Arp2/3 complex are critical for cell migration. The WAVE complex is the major Arp2/3 activator at the leading edge of migrating cells. However, multiple distinct WAVE complexes can be assembled in a cell, due to the combinatorial complexity of paralogous subunits. When systematically analyzing the contribution of each WAVE complex subunit to the metastasis-free survival of breast cancer patients, we found that overexpression of the CYFIP2 subunit was surprisingly associated with good prognosis. Gain and loss of function experiments in transformed and untransformed mammary epithelial cells revealed that cell migration was always inversely related to CYFIP2 levels. The role of CYFIP2 was systematically opposite to the role of the paralogous subunit CYFIP1 or of the NCKAP1 subunit. The specific CYFIP2 function in inhibiting cell migration was related to its unique ability to down-regulate classical pro-migratory WAVE complexes. The anti-migratory function of CYFIP2 was also revealed in migration of prechordal plate cells during gastrulation of the zebrafish embryo, indicating that the unique function of CYFIP2 is critically important in both physiological and pathophysiological migrations.

Published
2020

47. Logistic Regression with Missing Covariates -- Parameter Estimation, Model Selection and Prediction

Author

Jiang, Wei, Josse, Julie, Lavielle, Marc, Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Modélisation en pharmacologie de population (XPOP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
major trauma, [STAT]Statistics [stat], incomplete data, public health, observed likelihood, Metropolis-Hastings, [STAT.ME]Statistics [stat]/Methodology [stat.ME], variable selection
Abstract

International audience; Logistic regression is a common classification method in supervised learning. Surprisingly , there are very few solutions for performing it and selecting variables in the presence of missing values. We develop a complete approach, including the estimation of parameters and variance of estimators, derivation of confidence intervals and a model selection procedure, for cases where the missing values can be anywhere in covariates. By well organizing different patterns of missingness in each observation , we propose a stochastic approximation version of the EM algorithm based on Metropolis-Hasting sampling, to perform statistical inference for logistic regression with incomplete data. We also tackle the problem of prediction for a new individual with missing values, which is never addressed. The methodology is computationally efficient, and its good coverage and variable selection properties are demonstrated in a simulation study where we contrast its performances to other methods. For instance, the popular multiple imputation by chained equation can lead to biased estimates while our method is unbiased. We then illustrate the method on a dataset of severely traumatized patients from Paris hospitals to predict the occurrence of hemorrhagic shock, a leading cause of early preventable death in severe trauma cases. The aim is to consolidate the current red flag procedure, a binary alert identifying patients with a high risk of severe hemorrhage. The methodology is implemented in the R package misaem.

Published
2019

48. On the Convergence Properties of the Mini-Batch EM and MCEM Algorithms

Author

Belhal Karimi, Lavielle, Marc, Moulines, Eric, Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [MATH]Mathematics [math]
Abstract

The EM algorithm is one of the most popular algorithm for inference in latent data models. For large datasets, each iteration of the algorithm can be numerically involved. To alleviate this problem, (Neal and Hinton, 1998) has proposed an incremental version in which the conditional expectation of the latent data (E-step) is computed on a mini-batch of observations. In this paper, we analyse this variant and propose and analyse the Monte Carlo version of the incremental EM in which the conditional expectation is evaluated by a Markov Chain Monte Carlo (MCMC). We establish the almost-sure convergence of these algorithms, covering both the mini-batch EM and its stochastic version. Various numerical applications are introduced in this article to illustrate our findings.

Published
2019

49. Semiparametric estimation of the frequency of unknown periodic functions and its application to laser vibrometry signals

Author

Lavielle, Marc and Levy-Leduc, Celine

Subjects
Wave frequency -- Analysis, Signal processing -- Analysis, Parameter estimation -- Analysis, Digital signal processor, Business, Computers, Electronics, Electronics and electrical industries
Abstract

A semiparametric approach to fundamental frequency estimation of an unknown periodic signal in additive white noise based on model selection is proposed. It is shown on simulated data that the penalized cumulated periodogram yields an accurate estimation of the frequencies at very low signal-to-noise ratios.

Published
2005

Catalog

Books, media, physical & digital resources
See catalog results