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In this paper, we construct a moment inequality for mixing dependent random variables, it is of independent interest. As applications, the consistency of the kernel density estimation is investigated....
We present some new density estimation algorithms obtainedby bootstrap ag-gregation like Bagging. Our algorithms are analyzed and empirically compared to other methods found in the statistical literat...
Let $X_1,...,X_n$ be a random sample from some unknown probability density $f$ defined on a compact homogeneous manifold $\mathbf M$ of dimension $d \ge 1$. Consider a 'needlet frame' $\{\phi_{j \eta}...
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 study the nonparametric spline density estimators of probability density. The equivalence of weak convergence for L,-consistency of one density and completely for L,-consistency of all densities...
We construct a data-driven projection density estimator for continuous time processes. This estimator reaches superoptimal rates over a class F0 of densities that is dense in the family of all possibl...

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