[Statlist] Fwd: Research seminar in statistics February 27th 2015, GSEM University of Geneva

[Eva Cantoni]

Dear All,

Please note that the seminar scheduled this Friday February 27th, 2015 
is cancelled.

Look forward to seeing you at our next seminar with Reto Bürgin on 
Friday 13th March.


Best regards,


Karen Longden Roure
Administrative Support to
MSc. in Management, MSc. In Economics, MSc. In Statistics
Université de Genève, Uni-Mail
Faculté d'Economie & Management, GSEM
40, bd. du Pont d'Arve, 1211 Genève 4
Tél:  +41.22.379.8109 (10h-14h)
Fax: +41.22.379.8104

-------- Forwarded Message --------
Subject: 	[Statlist] Research seminar in statistics February 27th 2015, 
GSEM University of Geneva
Date: 	Mon, 23 Feb 2015 09:09:40 +0100
From: 	Eva Cantoni <Eva.Cantoni using unige.ch>
To: 	statlist using stat.ch



E-mail from the  Statlist using stat.ch  mailing list
_________________________________________________
Organisers :
E. Cantoni - E. Ronchetti - S. Sperlich - M-P. Victoria-Feser

Friday February 27th, 2015
at 11h15 - Room M 5220, Uni Mail (40, bd du Pont-d'Arve)

Julie Josse
Agrocampus Ouest, Rennes

ABSTRACT :
Low-rank matrix estimation plays a key role in many scientific and
engineering tasks including collaborative filtering and image
denoising.  Low-rank procedures are often motivated by the statistical
model where we observe a noisy matrix drawn from some distribution with
expectation assumed to have a low-rank representation. The statistical
goal is to try to recover the signal from the noisy data. Classical
approaches are centered around singular-value decomposition algorithms.
Although the truncated singular value decomposition has been extensively
used and studied, the estimator is found to be noisy and its performance
can be improved by regularization. Methods based on singular-value
shrinkage have achieved considerable empirical success and also have
provable optimality properties in the Gaussian noise model (Gavish &
Donoho, 2014). In this presentation, we propose a new framework for
regularized low-rank estimation that does not start from the
singular-value shrinkage point of view. Our approach is motivated by a
simple parametric boostrap idea. In the simplest case of isotropic
Gaussian noise, we end up with a new singular-value shrinkage estimator
whereas for non-isotropic noise models, our procedure yields new
estimators that perform well in experiments. This is a joint work with
Stefan Wager.

Visit the website: http://www.stat-center.unige.ch/ressem.html

-- 
Prof. Eva Cantoni
Research Center for Statistics and
       Geneva School of Economics and Management
University of Geneva, Bd du Pont d'Arve 40, CH-1211 Genève 4
http://stat-center.unige.ch/members2/profs/eva-cantoni/

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