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SUMMARY:Adaptive and sparse estimation in the functional linear model
DTSTART:20220506T153000
DTEND:20220506T170000
DTSTAMP:20260505T131002Z
UID:45fab22d831499fe02918dbcf4ef736184472403eff82eb64985a1ed
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Angelina Roche\, CERAMADE\, Université Paris Dauphiné\nT
 he aim of functional data statistics is to study data that can be represen
 ted as curves (temperature\, electricity consumption\,...). \nThe aim of 
 this talk is to present recent works on estimation in the functional linea
 r model which is a linear model whose covariates are functional data. \nW
 e first study the case where there is only one functional covariate and pr
 opose a projection estimator based on PCA. We define a data-driven criteri
 on for selecting the dimension of the projection space and show that the e
 stimator selected achieves the optimal convergence rate in the minimax sen
 se. \nIn a second part\, we will focus on the multivariate functional lin
 ear model where the covariates are multiple and can be of different nature
  and we will study the theoretical properties of a variant of Lasso. \nTh
 e method will be motivated and illustrated on simulated and real data sets
 .
LOCATION:MA A1 10 https://plan.epfl.ch/?room==MA%20A1%2010 https://epfl.zo
 om.us/j/66136073806
STATUS:CONFIRMED
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