Nonparametric adaptive estimation for grouped data

Abstract : The aim of this paper is to estimate the density f of a random variable X when one has access to independent observations of the sum of K ≥ 2 independent copies of X. We provide a constructive estimator based on a suitable definition of the logarithm of the empirical characteristic function. We propose a new strategy for the data driven choice of the cut-off parameter. The adaptive estimator is proven to be minimax-optimal up to some logarithmic loss. A numerical study illustrates the performances of the method. Moreover, we discuss the fact that the definition of the estimator applies in a wider context than the one considered here.
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Journal of Statistical Planning and Inference, Elsevier, 2017, 182, pp.12--28. <10.1016/j.jspi.2016.10.002>
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Soumis le : vendredi 3 juin 2016 - 15:25:57
Dernière modification le : mercredi 1 février 2017 - 16:11:36

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Céline Duval, Johanna Kappus. Nonparametric adaptive estimation for grouped data. Journal of Statistical Planning and Inference, Elsevier, 2017, 182, pp.12--28. <10.1016/j.jspi.2016.10.002>. <hal-01245781v2>

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