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2020 – today
- 2024
[j24]Rolando De la Cruz
, Marc Lavielle, Cristian Meza, Vicente Núñez-Antón:
A joint analysis proposal of nonlinear longitudinal and time-to-event right-, interval-censored data for modeling pregnancy miscarriage. Comput. Biol. Medicine 182: 109186 (2024)- 2022
- [j23]Marc Lavielle:
Using hospital data for monitoring the dynamics of COVID-19 in France. J. Data Sci. Stat. Vis. 2(7): 46-62 (2022) - 2020
- [j22]Belhal Karimi, Marc Lavielle, Eric Moulines:
f-SAEM: A fast stochastic approximation of the EM algorithm for nonlinear mixed effects models. Comput. Stat. Data Anal. 141: 123-138 (2020) - [j21]Wei Jiang, Julie Josse, Marc Lavielle, TraumaBase Group:
Logistic regression with missing covariates - Parameter estimation, model selection and prediction within a joint-modeling framework. Comput. Stat. Data Anal. 145: 106907 (2020)
2010 – 2019
- 2019
- [j20]Aline Marguet, Marc Lavielle, Eugenio Cinquemani:
Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data. Bioinform. 35(14): i586-i595 (2019) - [c4]Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle:
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods. NeurIPS 2019: 2833-2843 - [i1]Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle:
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods. CoRR abs/1910.12521 (2019) - 2014
- [j19]Marc Lavielle, Cyprien Mbogning:
An improved SAEM algorithm for maximum likelihood estimation in mixtures of non linear mixed effects models. Stat. Comput. 24(5): 693-707 (2014) - 2012
- [j18]Maud Delattre, Marc Lavielle:
Maximum likelihood estimation in discrete mixed hidden Markov models using the SAEM algorithm. Comput. Stat. Data Anal. 56(6): 2073-2085 (2012)
2000 – 2009
- 2009
- [c3]Merlin Keller, Marc Lavielle, Matthieu Perrot, Alexis Roche:
Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis. MICCAI (1) 2009: 450-457 - 2007
- [j17]Marc Lavielle, Cristian Meza:
A parameter expansion version of the SAEM algorithm. Stat. Comput. 17(2): 121-130 (2007) - 2006
- [j16]Adeline Samson, Marc Lavielle, France Mentré:
Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model. Comput. Stat. Data Anal. 51(3): 1562-1574 (2006) - [j15]Sophie Donnet, Marc Lavielle, Jean-Baptiste Poline:
Are fMRI event-related response constant in time? A model selection answer. NeuroImage 31(3): 1169-1176 (2006) - 2005
- [j14]Franck Picard, Stéphane Robin, Marc Lavielle, Christian Vaisse, Jean-Jacques Daudin:
A statistical approach for array CGH data analysis. BMC Bioinform. 6: 27 (2005) - [j13]Estelle Kuhn, Marc Lavielle:
Maximum likelihood estimation in nonlinear mixed effects models. Comput. Stat. Data Anal. 49(4): 1020-1038 (2005) - [j12]Marc Lavielle:
Using penalized contrasts for the change-point problem. Signal Process. 85(8): 1501-1510 (2005) - [j11]Marc Lavielle, Céline Lévy-Leduc:
Semiparametric estimation of the frequency of unknown periodic functions and its application to laser vibrometry signals. IEEE Trans. Signal Process. 53(7): 2306-2314 (2005) - [c2]Mohamed Sahmoudi, Karim Abed-Meraim, Marc Lavielle, Estelle Kuhn, Philippe Ciblat:
Blind source separation of noisy mixtures using a semi-parametric approach with application to heavy-tailed signals. EUSIPCO 2005: 1-4 - 2004
- [c1]Sophie Donnet, Marc Lavielle, Philippe Ciuciu, Jean-Baptiste Poline:
Selection of Temporal Models for Event-Related fMRI. ISBI 2004: 992-995 - 2003
- [j10]Jean-Yves Tourneret, Michel Doisy, Marc Lavielle:
Bayesian off-line detection of multiple change-points corrupted by multiplicative noise: application to SAR image edge detection. Signal Process. 83(9): 1871-1887 (2003) - 2001
- [j9]Marc Lavielle, E. Lebarbier:
An application of MCMC methods for the multiple change-points problem. Signal Process. 81(1): 39-53 (2001)
1990 – 1999
- 1999
- [j8]Olivier Cappé, Arnaud Doucet, Marc Lavielle, Eric Moulines:
Simulation-based methods for blind maximum-likelihood filter identification. Signal Process. 73(1-2): 3-25 (1999) - [j7]Marc Lavielle, D. Marquez:
Gibbs sampling for parameters estimation and change-points detection in inverse problems. Application to electromagnetic imaging. Signal Process. 78(3): 349-362 (1999) - 1998
- [j6]Marc Lavielle:
Optimal segmentation of random processes. IEEE Trans. Signal Process. 46(5): 1365-1373 (1998) - 1997
- [j5]Marc Lavielle, Eric Moulines:
A simulated annealing version of the EM algorithm for non-Gaussian deconvolution. Stat. Comput. 7(4): 229-236 (1997) - 1995
- [j4]Marc Lavielle:
A stochastic algorithm for parametric and non-parametric estimation in the case of incomplete data. Signal Process. 42(1): 3-17 (1995) - 1994
- [j3]Fabrice Gamboa, Marc Lavielle:
On two-dimensional spectral realization. IEEE Trans. Inf. Theory 40(5): 1603-1608 (1994) - 1993
- [j2]Marc Lavielle:
Bayesian deconvolution of Bernoulli-Gaussian processes. Signal Process. 33(1): 67-79 (1993) - [j1]Marc Lavielle:
Detection of changes in the spectrum of a multidimensional process. IEEE Trans. Signal Process. 41(2): 742-749 (1993)
Coauthor Index
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