Fabien Navarro

We don’t have enough information about this author to calculate their statistics. If you think this is an error let us know.
Learn More
We investigate the estimation of a weighted density taking the form g = w(F)f , where f denotes an unknown density, F the associated distribution function and w is a known non-negative weight. Such a class encompasses many examples, including those arising in order statistics or when g is related to the maximum or the minimum of N (random or fixed)(More)
We observe n heteroscedastic stochastic processes {Yv(t)}v, where for any v ∈ {1,. .. , n} and t ∈ [0, 1], Yv(t) is the convolution product of an unknown function f and a known blurring function gv corrupted by Gaussian noise. Under an ordinary smoothness assumption on g 1 ,. .. , gn, our goal is to estimate the d-th derivatives (in weak sense) of f from(More)
The problem of estimating the density-weighted average derivative of a regression function is considered. We present a new consistent estimator based on a plug-in approach and wavelet projections. Its performances are explored under various dependence structures on the observations: the independent case, the ρ-mixing case and the α-mixing case. More(More)
  • 1