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We consider the model of non-regular nonparametric regression where smoothness constraints are imposed on the regression function and the regression errors are assumed to decay with some sharpness lev...
In this thesis we study adaptive nonparametric regression with noise misspecifi-cation and the complexity of approximation of random fields in dependence of the dimension. First, we consider the prob...
In this article we consider the estimation of the joint distribution of the random coefficients and error term in the nonparametric random coefficients binary choice model. In this model from economic...
This paper studies the estimation of a large covariance matrix. We introduce a novel procedure called ChoSelect based on the Cholesky factor of the inverse covariance. This method uses a dimension red...
The aim of this paper is to provide a new method for the detection of either favored or avoided distances between genomic events along DNA sequences. These events are modeled by a Hawkes’ process. ...
We show that under a linearity condition on the distribution of the predictors, the coefficient vector in a single-index regression can be estimated with the same efficiency as in the case when the ...
In this paper we obtain convergence rates for sieved maximum-likelihood &timators of the log-hazard function in a censoring model. We also establish convergence results for an adaptive version of t...
This paper describe6 the adaptive estimation problem based on ranks for the parameter of an ARMA process. Tbe local asymptotic normality property with a ranked based central sequence allows for the...
We propose an estimation procedure for linear functionals based on Gaussian model selection techniques. We show that the procedure is adaptive, and we give a non asymptotic oracle inequality for the r...
We consider the model Zi = Xi + εi, for i.i.d. Xi’s and εi’s and independent sequences (Xi)i2Nand (εi)i2N. The density f" of ε1 is assumed to be known, whereas the one of X1, denoted by g, is unknown...
We introduce a wavelet-based model of local stationarity. This model enlarges the class of locally stationary wavelet processes and contains processes whose spectral density function may change very...
Adaptive estimation of a quadratic functional over both Besov and Lp balls is considered. A collection of nonquadratic estimators are developed which have useful bias and variance properties over i...

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