Dependent Lindeberg central limit theorem and some applications

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dc.creatorBardet, Jean-Marc
dc.creatorDoukhan, Paul
dc.creatorLang, Gabriel
dc.creatorRagache, Nicolas
dc.date2007-01-30
dc.date.accessioned2026-07-07T08:08:39Z
dc.date.available2026-07-07T08:08:39Z
dc.descriptionIn this paper, a very useful lemma (in two versions) is proved: it simplifies notably the essential step to establish a Lindeberg central limit theorem for dependent processes. Then, applying this lemma to weakly dependent processes introduced in Doukhan and Louhichi (1999), a new central limit theorem is obtained for sample mean or kernel density estimator. Moreover, by using the subsampling, extensions under weaker assumptions of these central limit theorems are provided. All the usual causal or non causal time series: Gaussian, associated, linear, ARCH($\infty$), bilinear, Volterra processes,$...$, enter this frame.
dc.identifierhttps://arxiv.org/abs/math/0701872
dc.identifierhttp://arxiv.org/abs/math/0701872
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131336
dc.subjectStatistics Theory
dc.titleDependent Lindeberg central limit theorem and some applications
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