A quantile-copula approach to conditional density estimation

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dc.creatorFaugeras, Olivier P.
dc.date2007-09-20
dc.date2008-06-12
dc.date.accessioned2026-07-07T09:44:04Z
dc.date.available2026-07-07T09:44:04Z
dc.descriptionWe present a new non-parametric estimator of the conditional density of the kernel type. It is based on an efficient transformation of the data by quantile transform. By use of the copula representation, it turns out to have a remarkable product form. We study its asymptotic properties and compare its bias and variance to competitors based on nonparametric regression.
dc.descriptionwith short simulations
dc.identifierhttps://arxiv.org/abs/0709.3192
dc.identifierhttp://arxiv.org/abs/0709.3192
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/162736
dc.subjectMethodology
dc.subjectStatistics Theory
dc.subject62G007, 62M20, 62M10
dc.titleA quantile-copula approach to conditional density estimation
dc.typetext

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