The stochastic approximation method for the estimation of a multivariate probability density

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dc.creatorMokkadem, Abdelkader
dc.creatorPelletier, Mariane
dc.creatorSlaoui, Yousri
dc.date2008-07-18
dc.date.accessioned2026-07-07T09:51:18Z
dc.date.available2026-07-07T09:51:18Z
dc.descriptionWe apply the stochastic approximation method to construct a large class of recursive kernel estimators of a probability density, including the one introduced by Hall and Patil (1994). We study the properties of these estimators and compare them with Rosenblatt's nonrecursive estimator. It turns out that, for pointwise estimation, it is preferable to use the nonrecursive Rosenblatt's kernel estimator rather than any recursive estimator. A contrario, for estimation by confidence intervals, it is better to use a recursive estimator rather than Rosenblatt's estimator.
dc.description28 pages
dc.identifierhttps://arxiv.org/abs/0807.2960
dc.identifierhttp://arxiv.org/abs/0807.2960
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/165242
dc.subjectStatistics Theory
dc.subject62G07; 62L20
dc.titleThe stochastic approximation method for the estimation of a multivariate probability density
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