Birgé Lucien, Hardle Wolfgang, Lepski Oleg, Picard Dominique, Wellner JonWe study two statistics models: the regression model with random design and the Gaussian white noise model. In these models, the goal is to estimate in sup-norm an unknown function f, from the observations, supposing that f belongs to a Holder class. In the regression model, for the estimation of a one-dimensional function, we obtain the exact constant and an asymptotically exact estimator. In the Gaussian white noise model, we study the estimation on two classes of multidimensional anisotropic functions, which one is an additive class. For these two classes, we determine the exact constant and an asymptotically exact estimator, and we show the link with "optimal recov...
Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regula...
Consider an autoregressive model with measurement error: we observe Zi = Xi + ...
We consider the problem of adaptive point-wise estimation of an unknown regression function f(x) who...
Birgé Lucien, Hardle Wolfgang, Lepski Oleg, Picard Dominique, Wellner JonWe study two statistics mod...
We consider the Gaussian White Noise Model and we study the estimation of a function f in the unifor...
We consider the Gaussian White Noise Model and we study the estimation of a function f in the unifor...
This thesis is devoted to the study of statistical problems of non parametrical estimation. A noisy ...
This thesis is devoted to the study of statistical problems of non parametrical estimation. A noisy ...
À partir des observations Z(n) = {(Xi, Yi), i = 1, ..., n} satisfaisant Yi = f(Xi) + ζi, nous voulon...
This thesis takes place within the theories of nonasymptotic statistics and model selection. Its goa...
International audienceIn This work, the asymptotic estimation of a class F of functional parameters ...
International audienceIn This work, the asymptotic estimation of a class F of functional parameters ...
This thesis presents new statistical procedures in a non-parametric framework and studies both their...
This thesis presents new statistical procedures in a non-parametric framework and studies both their...
We consider the problem of estimating an unknown function at a fixed point in nonparametric regressi...
Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regula...
Consider an autoregressive model with measurement error: we observe Zi = Xi + ...
We consider the problem of adaptive point-wise estimation of an unknown regression function f(x) who...
Birgé Lucien, Hardle Wolfgang, Lepski Oleg, Picard Dominique, Wellner JonWe study two statistics mod...
We consider the Gaussian White Noise Model and we study the estimation of a function f in the unifor...
We consider the Gaussian White Noise Model and we study the estimation of a function f in the unifor...
This thesis is devoted to the study of statistical problems of non parametrical estimation. A noisy ...
This thesis is devoted to the study of statistical problems of non parametrical estimation. A noisy ...
À partir des observations Z(n) = {(Xi, Yi), i = 1, ..., n} satisfaisant Yi = f(Xi) + ζi, nous voulon...
This thesis takes place within the theories of nonasymptotic statistics and model selection. Its goa...
International audienceIn This work, the asymptotic estimation of a class F of functional parameters ...
International audienceIn This work, the asymptotic estimation of a class F of functional parameters ...
This thesis presents new statistical procedures in a non-parametric framework and studies both their...
This thesis presents new statistical procedures in a non-parametric framework and studies both their...
We consider the problem of estimating an unknown function at a fixed point in nonparametric regressi...
Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regula...
Consider an autoregressive model with measurement error: we observe Zi = Xi + ...
We consider the problem of adaptive point-wise estimation of an unknown regression function f(x) who...