Convergence rates for density estimators of weakly dependent time series

dc.creatorRagache, Nicolas
dc.creatorWintenberger, Olivier
dc.date2006-03-10
dc.date2007-01-10
dc.date.accessioned2026-07-07T08:07:39Z
dc.date.available2026-07-07T08:07:39Z
dc.descriptionAssuming that $(X_t)_{t\in\Z}$ is a vector valued time series with a common marginal distribution admitting a density $f$, our aim is to provide a wide range of consistent estimators of $f$. We consider different methods of estimation of the density as kernel, projection or wavelets ones. Various cases of weakly dependent series are investigated including the Doukhan & Louhichi (1999)'s $η$-weak dependence condition, and the $\tilde ϕ$-dependence of Dedecker & Prieur (2005). We thus obtain results for Markov chains, dynamical systems, bilinear models, non causal Moving Average... From a moment inequality of Doukhan & Louhichi (1999), we provide convergence rates of the term of error for the estimation with the $Ł^q$ loss or almost surely, uniformly on compact subsets.
dc.identifierhttps://arxiv.org/abs/math/0603254
dc.identifierhttp://arxiv.org/abs/math/0603254
dc.identifierDependence in Probability and Statistics. Springer (Ed.) (2006) 380
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131005
dc.subjectStatistics Theory
dc.subjectProbability
dc.titleConvergence rates for density estimators of weakly dependent time series
dc.typetext

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