On adaptive wavelet estimation of a class of weighted densities

Abstract : 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) independent and identically distributed (\iid) random variables. We here construct a new adaptive non-parametric estimator for $g$ based on a plug-in approach and the wavelets methodology. For a wide class of models, we prove that it attains fast rates of convergence under the $\mathbb{L}_p$ risk with $p\ge 1$ (not only for $p = 2$ corresponding to the mean integrated squared error) over Besov balls. The theoretical findings are illustrated through several simulations.
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Communications in Statistics - Simulation and Computation, Taylor & Francis, 2014, 12 p. 〈10.1080/03610918.2013.851216〉
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Fabien Navarro, Christophe Chesneau, Jalal M. Fadili. On adaptive wavelet estimation of a class of weighted densities. Communications in Statistics - Simulation and Computation, Taylor & Francis, 2014, 12 p. 〈10.1080/03610918.2013.851216〉. 〈hal-00714507v4〉

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