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Optimal upper and lower bounds for the true and empirical excess risks
  in heteroscedastic least-squares regression

Optimal upper and lower bounds for the true and empirical excess risks in heteroscedastic least-squares regression

24 April 2013
Adrien Saumard
ArXivPDFHTML

Papers citing "Optimal upper and lower bounds for the true and empirical excess risks in heteroscedastic least-squares regression"

4 / 4 papers shown
Title
Optimal model selection in density estimation
Optimal model selection in density estimation
M. Lerasle
110
46
0
09 Oct 2009
Data-driven calibration of linear estimators with minimal penalties
Data-driven calibration of linear estimators with minimal penalties
Sylvain Arlot
Francis R. Bach
136
61
0
10 Sep 2009
Data-driven calibration of penalties for least-squares regression
Data-driven calibration of penalties for least-squares regression
Sylvain Arlot
P. Massart
194
159
0
06 Feb 2008
Rejoinder: 2004 IMS Medallion Lecture: Local Rademacher complexities and
  oracle inequalities in risk minimization
Rejoinder: 2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization
V. Koltchinskii
566
196
0
01 Aug 2007
1