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UID:9062@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20260330T140000
DTEND;TZID=Europe/Paris:20260330T150000
DTSTAMP:20260327T102144Z
URL:https://www.i2m.univ-amu.fr/evenements/seminaire-destelle-kuhn-2/
SUMMARY:Estelle Kuhn (INRAE - Université Paris Saclay): Séminaire d'Estel
 le Kuhn
DESCRIPTION:Estelle Kuhn: \n\n\nEstimation and variable selection in high d
 imension in nonlinear mixed-effects models\n\nJoint work with Antoine Cai
 llebotte (INRAE\, MaIAGE\, GQE-Le Moulon) and Sarah Lemler (CentraleSup
 elec\, MICS)\n \nIn this work\, we consider nonlinear mixed-effects model
 s including high-dimensional covariates to model individual parameters var
 iability. The objective is to identify relevant covariates among a large s
 et under sparsity assumption and to estimate model parameters. To face the
  high dimensional setting\, we consider a regularized estimator namely the
  maximum likelihood estimator penalized with the l1-penalty. We rely on th
 e use of the eBIC model choice criterium to select an optimal reduced mode
 l. Then we estimate the parameters by maximizing the likelihood of the red
 uced model. We calculate in practice the maximum likelihood estimator pena
 lized with the l1-penalty though a weighted proximal stochastic gradient d
 escent algorithm with an adaptive learning rate. This choice allows us to 
 consider very general models\, in particular models that do not belong to 
 the curved exponential family. We demonstrate first in a simple linear toy
  model through a simulation study the good convergence properties of this 
 optimization algorithm. We compare then the performance of the proposed me
 thodology with those of the glmmLasso procedure in a linear mixed-effects 
 model in a simulation study. We illustrate also its performance in a nonli
 near mixed-effects logistic growth model through simulation. We highlight 
 the benefit of the proposed procedure relying on this integrated single st
 ep approach regarding two others two steps approaches for variable selecti
 on objective in mixed models. Finally we analyze real data of wheat senesc
 ence to identify potential relevant markers of this biological process.\n\
 n
CATEGORIES:Séminaire,Statistique
LOCATION:Saint-Charles - FRUMAM  (2ème étage)\, 3 Place Victor Hugo\, Mar
 seille\, 13003\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=3 Place Victor Hugo\, Marse
 ille\, 13003\, France;X-APPLE-RADIUS=100;X-TITLE=Saint-Charles - FRUMAM  (
 2ème étage):geo:0,0
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DTSTART:20260329T030000
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