Add to ORKGIn this paper, we observe a sparse mean vector through Gaussian noise and we aim at estimating some additive functional of the mean in the minimax sense. More precisely, we generalize the results of (Collier et al., 2017, 2019) to a very large class of functionals. The optimal minimax rate is shown to depend on the polynomial approximation rate of the marginal functional, and optimal estimators achieving this rate are built
International audienceWe consider two problems of estimation in high-dimensional Gaussian models. Th...
The problem of predicting integrals of stochastic processes is considered. Linear estimators have b...
In this thesis we study adaptive methods of estimation for two particular types of statistical prob...
In this paper, we observe a sparse mean vector through Gaussian noise and we aim at estimating some ...
For the Gaussian sequence model, we obtain non-asymp-totic minimax rates of estimation of the linear...
International audienceWe consider the problem of estimation of a linear functional in the Gaussian s...
We study the problem of estimation of the value N_gamma(theta) = sum(i=1)^d |theta_i|^gamma for 0 0...
The present paper considers the problem of estimating a linear functional φ = ∫∞ -∞ φ(x)f (x)dx of a...
Minimax bounds for the risk function of estimators of functionals of the spectral density of Gaussia...
We consider the estimation of quadratic functionals in a Gaussian sequence model where the eigenvalu...
The minimax theory for estimating linear functionals is extended to the case of a finite union of co...
Abstract. High-dimensional statistical tests often ignore correlations to gain simplicity and stabil...
We propose a general framework for the construction and analysis of minimax estimators for a wide cl...
Abstract. Minimax bounds for the risk function of estimators of functionals of the spectral density ...
High-dimensional statistical tests often ignore correlations to gain simplicity and stability leadin...
International audienceWe consider two problems of estimation in high-dimensional Gaussian models. Th...
The problem of predicting integrals of stochastic processes is considered. Linear estimators have b...
In this thesis we study adaptive methods of estimation for two particular types of statistical prob...
In this paper, we observe a sparse mean vector through Gaussian noise and we aim at estimating some ...
For the Gaussian sequence model, we obtain non-asymp-totic minimax rates of estimation of the linear...
International audienceWe consider the problem of estimation of a linear functional in the Gaussian s...
We study the problem of estimation of the value N_gamma(theta) = sum(i=1)^d |theta_i|^gamma for 0 0...
The present paper considers the problem of estimating a linear functional φ = ∫∞ -∞ φ(x)f (x)dx of a...
Minimax bounds for the risk function of estimators of functionals of the spectral density of Gaussia...
We consider the estimation of quadratic functionals in a Gaussian sequence model where the eigenvalu...
The minimax theory for estimating linear functionals is extended to the case of a finite union of co...
Abstract. High-dimensional statistical tests often ignore correlations to gain simplicity and stabil...
We propose a general framework for the construction and analysis of minimax estimators for a wide cl...
Abstract. Minimax bounds for the risk function of estimators of functionals of the spectral density ...
High-dimensional statistical tests often ignore correlations to gain simplicity and stability leadin...
International audienceWe consider two problems of estimation in high-dimensional Gaussian models. Th...
The problem of predicting integrals of stochastic processes is considered. Linear estimators have b...
In this thesis we study adaptive methods of estimation for two particular types of statistical prob...