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We provide a detailed study of the estimation of probability distributions---discrete and continuous---in a stringent setting in which data is kept private even from the statistician. We give sharp mi...
We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subse...
We consider the problem of estimating the unknown response function in the multichannel deconvolution model with long-range dependent Gaussian errors. We do not limit our consideration to a specific t...
We address the online linear optimization problem when the actions of the forecaster are represented by binary vectors. Our goal is to understand the magnitude of the minimax regret for the worst pos...
In this paper, we present a unified approach to function approximation in reproducing kernel Hilbert spaces (RKHS) that establishes a previously unrecognized optimality property for several well-known...
We propose MC+, a fast, continuous, nearly unbiased and accu- rate method of penalized variable selection in high-dimensional linear regression. The LASSO is fast and continuous, but biased. The bia...
A new lower bound involving f-divergences between the underlying probability measures is proved for the minimax risk in estimation problems. The proof just uses the convexity of the function f and ...
In this paper, we are interested in the study of beta kernel estimators from an asymptotic minimax point of view. It is well known that beta kernel estimators are—on the contrary of classical kernel...
We consider the signal detection problem for mildly, severely and extremely ill-posed inverse problems with Sobolev, analytic and generalized analytic classes of functions under the Gaussian white n...
Sur un theoreme de minimax et son application aux tests。
In this paper,following the robust Bayesian paradigm, a procedure based on the posterior regret-minimax principle is applied to derive,in a straightforwar way, new credibility formula,making use of ...
Minimax sequential estimation based on random fields。
We consider minimax trees with independent, identically distributed leaf values that have a continuous distribution function FV being strictly increasing on the range where 0< FV <1. It was shown by P...
The purpose of this paper is to study the problem of estimating a compactly supported function from noisy observations of its moments in an ill-posed inverse problem framework. We provide a statisti...

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