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Highly Cited

2013

Highly Cited

2013

Support vector machines (SVMs) using Gaussian kernels are one of the standard and state-of-the-art learning algorithms. In this… Expand

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Highly Cited

2013

Highly Cited

2013

We obtain sharp oracle inequalities for the empirical risk minimization procedure in the regression model under the assumption… Expand

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Highly Cited

2011

Highly Cited

2011

The purpose of these lecture notes is to provide an introduction to the general theory of empirical risk minimization with an… Expand

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Highly Cited

2011

Highly Cited

2011

We address the problem of density estimation with L s-loss by selection of kernel estimators. We develop a selection procedure… Expand

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Highly Cited

2010

Highly Cited

2010

We consider a finite mixture of regressions (FMR) model for high-dimensional inhomogeneous data where the number of covariates… Expand

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Highly Cited

2009

Highly Cited

2009

λ as an estimator of λ ∗ . They called this estimator “the Dantzig selector”. We study the properties of

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Highly Cited

2008

Highly Cited

2008

We explain some basic theoretical issues regarding nonparametric statistics applied to inverse problems. Simple examples are used… Expand

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Highly Cited

2006

Highly Cited

2006

Estimation methods for the Levy density of a Levy process are developed under mild qualitative assumptions. A classical model… Expand

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Highly Cited

2002

Highly Cited

2002

We consider a sequence space model of statistical linear inverse problems where we need to estimate a function f from indirect… Expand

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Highly Cited

2001

Highly Cited

2001

In this paper, we introduce nonlinear regularized wavelet estimators for estimating nonparametric regression functions when… Expand

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