Marc Lavielle

Jean-Baptiste Poline, Philippe Ciuciu, Marc Lavielle, 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

Dissemination - Scientific Communauty animation

Jean-Michel Marin, Gilles Celeux, Pascal Massart, 2008 .

Application Domains - Pharmacometrics

Marc Lavielle, Laura Brocco, Kevin Bleakley, 2013 .

A stochastic algorithm for parametric and non-parametric estimation in the case of incomplete data

Marc Lavielle, M. Lavielle, 1995, Signal Process..

Logistic regression with missing covariates - Parameter estimation, model selection and prediction within a joint-modeling framework

Wei Jiang, Julie Josse, Marc Lavielle, 2018, Comput. Stat. Data Anal..

Statistical aspects of environmental risk assessment of GM plants for effects on non-target organisms.

Hilko van der Voet, Kerstin Schmidt, Michele Scardi, 2009, Environmental biosafety research.

Estimation of Population Pharmacokinetic Parameters of Saquinavir in HIV Patients with the MONOLIX Software

Marc Lavielle, France Mentré, M. Lavielle, 2007, Journal of Pharmacokinetics and Pharmacodynamics.

Implementation and Evaluation of the SAEM Algorithm for Longitudinal Ordered Categorical Data with an Illustration in Pharmacokinetics–Pharmacodynamics

France Mentré, Radojka M. Savic, Marc Lavielle, 2010, The AAPS Journal.

Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis

Alexis Roche, Marc Lavielle, Merlin Keller, 2009, MICCAI.

Coupling the SAEM algorithm and the extended

Maud Delattre, Marc Lavielle, 2013 .

New Results - Neuroimaging, Statistical analysis of fMRI data

Jean-Michel Marin, Gilles Celeux, Jean-Michel Poggi, 2008 .

The use of the SAEM algorithm in MONOLIX software for estimation of population pharmacokinetic-pharmacodynamic-viral dynamics parameters of maraviroc in asymptomatic HIV subjects

Marc Lavielle, Lynn McFadyen, Phylinda L. S. Chan, 2010, Journal of Pharmacokinetics and Pharmacodynamics.

Software and Platforms - Monolix

Marc Lavielle, Célia Barthélémy, Hector Mesa, 2013 .

Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm

Marc Lavielle, Emmanuelle Comets, Audrey Lavenu, 2017 .

Simulation-based methods for blind maximum-likelihood filter identification

Eric Moulines, Arnaud Doucet, Olivier Cappé, 1999, Signal Process..

Automatic data binning for improved visual diagnosis of pharmacometric models

Marc Lavielle, Kevin Bleakley, K. Bleakley, 2011, Journal of Pharmacokinetics and Pharmacodynamics.

The SAEM algorithm for group comparison tests in longitudinal data analysis based on non‐linear mixed‐effects model

France Mentré, Marc Lavielle, Adeline Samson, 2007, Statistics in medicine.

Coupling the SAEM algorithm and the extended Kalman filter for maximum likelihood estimation in mixed-effects diffusion models

Maud Delattre, Marc Lavielle, M. Lavielle, 2013 .

Detection of changes in the spectrum of a multidimensional process

Marc Lavielle, 1993, IEEE Trans. Signal Process..

Bayesian deconvolution of Bernoulli-Gaussian processes

Marc Lavielle, M. Lavielle, 1993, Signal Process..

Maximum likelihood estimation in discrete mixed hidden Markov models using the SAEM algorithm

Maud Delattre, Marc Lavielle, 2012, Comput. Stat. Data Anal..

Mixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools

Marc Lavielle, M. Lavielle, 2014 .

Between-Subject and Within-Subject Model Mixtures for Classifying HIV Treatment Response

Marc Lavielle, Kevin Bleakley, Cyprien Mbogning, 2012 .

Maximum likelihood estimation of long-term HIV dynamic models and antiviral response.

France Mentré, Marc Lavielle, Adeline Samson, 2011, Biometrics.

A simulated annealing version of the EM algorithm for non-Gaussian deconvolution

Eric Moulines, Marc Lavielle, É. Moulines, 1997, Stat. Comput..

Optimal segmentation of random processes

Marc Lavielle, M. Lavielle, 1998, IEEE Trans. Signal Process..

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

Eric Moulines, Marc Lavielle, Hoi-To Wai, 2019, NeurIPS.

An application of MCMC methods for the multiple change-points problem

Marc Lavielle, Emilie Lebarbier, M. Lavielle, 2001, Signal Process..

Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model

France Mentré, Marc Lavielle, Adeline Samson, 2006, Comput. Stat. Data Anal..

The Standard Output: A Tool‐Agnostic Modeling Storage Format

France Mentré, Rikard Nordgren, Marc Lavielle, 2018, CPT: pharmacometrics & systems pharmacology.

Software - MONOLIX software

Marc Lavielle, Hector Mesa, Benoît Charles, 2008 .

A parameter expansion version of the SAEM algorithm

Marc Lavielle, Cristian Meza, M. Lavielle, 2007, Stat. Comput..

Gibbs sampling for parameters estimation and change-points detection in inverse problems. Application to electromagnetic imaging

Marc Lavielle, D. Marquez, M. Lavielle, 1999, Signal Process..

Exploring and Comparing Unsupervised Clustering Algorithms

Marc Lavielle, Philip D. Waggoner, M. Lavielle, 2020 .

Software and Platforms - Clinical trial simulator

Marc Lavielle, Laura Brocco, Fazia Bellal, 2013 .

Using penalized contrasts for the change-point problem

Marc Lavielle, M. Lavielle, 2005, Signal Process..

f-SAEM: A fast stochastic approximation of the EM algorithm for nonlinear mixed effects models

Marc Lavielle, Eric Moulines, Belhal Karimi, 2019, Comput. Stat. Data Anal..

R Speaks 'Monolix' [R package Rsmlx version 3.0.0]

Marc Lavielle, 2021 .

SAEMIX, an R version of the SAEM algorithm

Marc Lavielle, Emmanuelle Comets, Audrey Lavenu, 2012 .

Selection of a model of cerebral activity for fMRI Group Data analysis

Alexis Roche, Marc Lavielle, Merlin Keller, 2010, 1005.3225.

Title : Part II : A New SAEM Algorithm : Performance in Population Models for Count Data

Marc Lavielle, Radojka Savic, 2010 .

The Multiple Change-Points Problem for the Spectral Distribution

Marc Lavielle, Carenne Ludeña, M. Lavielle, 2000 .

Joint modelling of longitudinal and repeated time-to-event data using nonlinear mixed-effects models and the stochastic approximation expectation–maximization algorithm

Marc Lavielle, Kevin Bleakley, Cyprien Mbogning, 2015 .

A statistical approach for CGH microarray data analysis

Gilles Celeux, Franck Picard, Jean-Jacques Daudin, 2004 .

Modelling the influence of MDR1 polymorphism on digoxin pharmacokinetic parameters

France Mentré, Marc Lavielle, Emmanuelle Comets, 2007, European Journal of Clinical Pharmacology.

Pharmacometrics models with hidden Markovian dynamics

Marc Lavielle, M. Lavielle, 2018, Journal of Pharmacokinetics and Pharmacodynamics.

BIC selection procedures in mixed effects models

Maud Delattre, Marc Lavielle, Marie-Anne Poursat, 2012 .

A note on BIC in mixed-effects models

Maud Delattre, Marc Lavielle, Marie-Anne Poursat, 2014 .

Road trafficking description and short term travel time forecasting, with a classification method

Jean-Michel Loubes, Marc Lavielle, Elie Maza, 2006 .

Joint modelling of longitudinal and repeated time-to-event data using nonlinear mixed-effects models and the stochastic approximation expectation–maximization algorithm

Marc Lavielle, Kevin Bleakley, Cyprien Mbogning, 2015 .