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20062026
most citedSafe Feature Elimination in Sparse Supervised Learning

178 citations · 324 across the 24 of their papers we have counts for

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6 papers · 1 filter

math.ST2015★ 7 cited

Regression modeling on stratified data with the lasso

Edouard Ollier, Vivian Viallon

We consider the estimation of regression models on strata defined using a categorical covariate, in order to identify interactions between this categorical covariate and the other…

math.ST2008

Testing additivity in nonparametric regression under random censorship

Mohammed Debbarh, Vivian Viallon

In this paper, we are concerned with nonparametric estimation of the multivariate regression function in the presence of right censored data. More precisely, we propose a statistic…

math.ST2007

Uniform limit laws of the logarithm for nonparametric estimators of the regression function in presence of censored data

Bertrand Maillot, Vivian Viallon

In this paper, we establish uniform-in-bandwidth limit laws of the logarithm for nonparametric Inverse Probability of Censoring Weighted (I.P.C.W.) estimators of the multivariate r…

math.ST2007★ 1 cited

Uniform limit laws of the logarithm for estimators of the additive regression function in the presence of right censored data

Mohammed Debbarh, Vivian Viallon

It has been recently shown that nonparametric estimators of the additive regression function could be obtained in the presence of right censoring by coupling the marginal integrati…

math.ST2006★ 2 cited

Asymptotic normality for estimators of the additive regression components under random censorship

M. Debbarh, V. Viallon

We establish asymptotic normality for estimators of the additive regression components under random censorship. To build our estimators, we couple the marginal integration method (…

math.ST2006★ 5 cited

Functional limit laws for the increments of the quantile process; with applications

Vivian Viallon

We establish a functional limit law of the logarithm for the increments of the normed quantile process based upon a random sample of size . We extend a limit law obtain…