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SUMMARY:Adaptive estimation of functionals under sparsity - Alexandre Tsyb
 akov (CREST: Centre de Recherche en Économie et Statistique)
DTSTART:20180118T094500Z
DTEND:20180118T103000Z
UID:TALK97792@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Adaptive estimation of functionals in sparse mean model and in
  sparse regression exhibits some interesting e&#11\;ffects. This talk focu
 ses on estimation of the L_2-norm of the target vector and of the variance
  of the noise. In the first problem\, the ignorance of the noise level cau
 ses changes in the rates. Moreover\, the form of the noise distribution al
 so infuences the optimal rate. For example\, the rates of estimating the v
 ariance di&#11\;ffer depending on whether the noise is Gaussian or sub-Gau
 ssian without a precise knowledge of the distribution. Finally\, for the s
 parse mean model\, the sub-Gaussian rate cannot be attained adaptively to 
 the noise level on classes of noise distributions with polynomial tails\, 
 independently on how fast is the polynomial decay. Joint work with O.Colli
 er and L.Comminges.
LOCATION:Seminar Room 1\, Newton Institute
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