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Adaptive density estimation under dependence

Abstract : Assume that $(X_t)_{t\in\Z}$ is a real valued time series admitting a common marginal density $f$ with respect to Lebesgue's measure. Donoho {\it et al.}~(1996) propose a near-minimax method based on thresholding wavelets to estimate $f$ on a compact set in an independent and identically distributed setting. The aim of the present work is to extend these results to general weak dependent contexts. Weak dependence assumptions are expressed as decreasing bounds of covariance terms and are detailed for different examples. The threshold levels in estimators $\widehat f_n$ depend on weak dependence properties of the sequence $(X_t)_{t\in\Z}$ through the constant. If these properties are unknown, we propose cross-validation procedures to get new estimators. These procedures are illustrated via simulations of dynamical systems and non causal infinite moving averages. We also discuss the efficiency of our estimators with respect to the decrease of covariances bounds.
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Contributor : Irène Gannaz Connect in order to contact the contributor
Submitted on : Wednesday, October 15, 2008 - 3:13:01 PM
Last modification on : Friday, May 6, 2022 - 4:50:07 PM
Long-term archiving on: : Thursday, September 23, 2010 - 4:50:11 PM


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Irène Gannaz, Olivier Wintenberger. Adaptive density estimation under dependence. ESAIM: Probability and Statistics, EDP Sciences, 2010, 14, pp.151-172. ⟨10.1051/ps:2008025⟩. ⟨hal-00012077v3⟩



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