Pointwise Adaptive Estimation of the MarginalDensity of a Weakly Dependent Process

Abstract : This paper is devoted to the estimation of the common marginal density function of weakly dependent processes. The accuracy of estimation is measured using pointwise risks. We propose a datadriven procedure using kernel rules. The bandwidth is selected using the approach of Goldenshluger and Lepski and we prove that the resulting estimator satisfies an oracle type inequality. The procedure is also proved to be adaptive (in a minimax framework) over a scale of H\"older balls for several types of dependence: stong mixing processes, $\lambda$-dependent processes or i.i.d. sequences can be considered using a single procedure of estimation. Some simulations illustrate the performance of the proposed method.
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Journal of Statistical Planning and Inference, Elsevier, 2017, 187 (115-129), 〈10.1016/j.jspi.2017.03.003〉
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https://hal.archives-ouvertes.fr/hal-01299483
Contributeur : Marie-Annick Guillemer <>
Soumis le : jeudi 7 avril 2016 - 16:59:26
Dernière modification le : vendredi 22 juin 2018 - 01:19:58

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Karine Bertin, Nicolas Klutchnikoff. Pointwise Adaptive Estimation of the MarginalDensity of a Weakly Dependent Process. Journal of Statistical Planning and Inference, Elsevier, 2017, 187 (115-129), 〈10.1016/j.jspi.2017.03.003〉. 〈hal-01299483〉

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