Prof. Dr. Yannick Baraud
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| Fakultät oder Zentrum | Fakultät für Naturwissenschaften, Technologie und Medizin | ||||
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Université du Luxembourg Maison du Nombre 6, Avenue de la Fonte L-4364 Esch-sur-Alzette |
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| Büroadresse | MNO, E05 0515-110 | ||||
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| Telefon | (+352) 46 66 44 5807 | ||||
| Fax | (+352) 46 66 44 35807 | ||||
Link to Yannick Baraud's webpage: http://math.uni.lu/baraud/Home.html
Last updated on: Dienstag, den 05. November 2019
The “ERA Chair in Mathematical Statistics and Data Science – SanDAL” is a prestigious grant awarded to the University of Luxembourg by the European Research Area (ERA) under the EU’s Horizon 2020 framework programme. With a budget of 2.5 million euros for the period 2019-2024, the university will create a high-level research group in mathematical statistics and data science.
For more information please consult SanDAL webpage
Last updated on: 31 Aug 2019
2020
2019
Can we trust L2-criteria and L2-losses?in Journal de la Société Française de Statistique (2019), 160(3),
2018
Rho-estimators revisited: general theory and applications;
in Ann. Statist. (2018), 46(6B), 3767--3804
2017
Une alternative robuste au maximum de vraisemblance: la $\rho$-estimation;
in J. SFdS (2017), 158(3), 1--26
A new method for estimation and model selection: $\rho$-estimation; ;
in Invent. Math. (2017), 207(2), 425--517
2016
Bounding the expectation of the supremum of an empirical process over a (weak) VC-major classin Electron. J. Stat. (2016), 10(2), 1709--1728
Rho-estimators for shape restricted density estimation;
in Stochastic Process. Appl. (2016), 126(12), 3888--3912
2014
Estimating composite functions by model selection;
in Ann. Inst. Henri Poincaré Probab. Stat. (2014), 50(1), 285--314
Estimator selection in the Gaussian setting; ;
in Ann. Inst. Henri Poincaré Probab. Stat. (2014), 50(3), 1092--1119
2013
2011
Estimator selection with respect to Hellinger-type risksin Probab. Theory Related Fields (2011), 151(1-2), 353--401
2010
A Bernstein-type inequality for suprema of random processes with applications to model selection in non-Gaussian regressionin Bernoulli (2010), 16(4), 1064--1085
2009
Estimating the intensity of a random measure by histogram type estimators;
in Probab. Theory Related Fields (2009), 143(1-2), 239--284
2005
Testing convex hypotheses on the mean of a Gaussian vector. Application to testing qualitative hypotheses on a regression function; ;
in Ann. Statist. (2005), 33(1), 214--257
2004
2003
2002
A new test of linear hypothesis in regression; ;
in Goodness-of-fit tests and model validity (Paris, 2000) (2002)
2001
Adaptive estimation in autoregression or $\beta$-mixing regression via model selection; ;
in Ann. Statist. (2001), 29(3), 839--875
2000
Model selection for regression on a fixed designin Probab. Theory Related Fields (2000), 117(4), 467--493
