Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/11474
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dc.contributor.authorAzevedo, Cecília-
dc.contributor.authorOliveira, Paulo Eduardo-
dc.date.accessioned2009-09-18T12:55:24Z-
dc.date.available2009-09-18T12:55:24Z-
dc.date.issued2000-
dc.identifier.citationPré-Publicações DMUC. 00-16 (2000)en_US
dc.identifier.urihttp://hdl.handle.net/10316/11474-
dc.description.abstractThe estimation of distribution functions of pairs of associated variables is addressed based on a kernel estimator. This problem is motivated by the need to approximate covariance functions appearing as the limiting covariances of the empirical process sequence. Results characterizing the asymptotics and convergence rates of the estimator are obtained. From these we derive the optimal bandwidth convergence rate, which is of order n-1 . Finally, we give conditions for the asymptotic normality of the finite dimensional distributions, characterizing their limit covariance matrix. Besides some usual conditions on the kernel function, the conditions typically impose a convenient decrease rate on the covariances Cov(X1 , X n ).en_US
dc.language.isoengen_US
dc.publisherCentro de Matemática da Universidade de Coimbraen_US
dc.rightsopenAccessen_US
dc.titleBivariante distribution function estimation for associated variablesen_US
dc.typepreprinten_US
item.fulltextCom Texto completo-
item.grantfulltextopen-
item.languageiso639-1en-
Appears in Collections:FCTUC Matemática - Artigos em Revistas Nacionais
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