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UID:2896@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20190426T140000
DTEND;TZID=Europe/Paris:20190426T150000
DTSTAMP:20190411T120000Z
URL:https://www.i2m.univ-amu.fr/evenements/a-coherent-framework-for-learni
 ng-spatiotemporal-piecewise-geodesic-trajectories-from-longitudinal-manifo
 ld-valued-data/
SUMMARY: (...): A coherent framework for learning spatiotemporal piecewise-
 geodesic trajectories from longitudinal manifold-valued data
DESCRIPTION:: Longitudinal studies are powerful tools to achieve a better u
 nderstanding of temporal progressions of biological or natural phenomenons
 . For instance\, efforts in chemotherapy monitoring rely more and more on 
 the understanding of the global disease progression and not only on punctu
 al states of health. Mixed effects models have proved their efficiency in 
 the study of longitudinal data sets\, especially for medical purposes. Thi
 s talks presents a nonlinear mixed effects model which allows to estimate 
 both a group-representative piecewise-geodesic trajectory and inter-indivi
 dual variability. This model provides a generic and coherent framework for
  studying longitudinal manifold-valued data.-Estimation is formulated as a
  well-defined and consistent Maximum A Posteriori (MAP). Numerically\, due
  to the non-linearity of the proposed model\, the MAP estimation of the pa
 rameters is performed through a stochastic version of the Expectation-Maxi
 mization algorithm. I will present a new version of the Stochastic-Approxi
 mation EM (SAEM) algorithm which prevent convergence toward local minima.h
 ttp://juliette.chevallier.perso.math.cnrs.fr
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