Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/7759
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dc.contributor.authorJacob, Pierre-
dc.contributor.authorOliveira, Paulo-
dc.date.accessioned2009-02-17T11:18:59Z-
dc.date.available2009-02-17T11:18:59Z-
dc.date.issued1999en_US
dc.identifier.citationStatistical Inference for Stochastic Processes. 2:3 (1999) 227-251en_US
dc.identifier.urihttp://hdl.handle.net/10316/7759-
dc.description.abstractAbstract Nonparametric inference for point processes is discussed by way histograms, which provide a nice tool for the analysis of on-line data. The construction of histograms depends on a sequence of partitions, which we take to be nonembedded. This is quite natural in what regards applications, but presents some theoretical problems. In another direction, we drop the usual independence assumption on the sample, replacing it by an association assumption. Under this setting, we study the convergence of the histogram, in probability and almost surely which, under association, depends on conditions on the covariance structure. In the final section we prove that the finite dimensional distributions converge in distribution to a Gaussian centered vector with a specified covariance. The main tool of analysis is a decomposition of second order moment measures.en_US
dc.language.isoengeng
dc.rightsopenAccesseng
dc.titleHistograms and Associated Point Processesen_US
dc.typearticleen_US
dc.identifier.doi10.1023/A:1009989902595en_US
item.grantfulltextopen-
item.fulltextCom Texto completo-
item.languageiso639-1en-
crisitem.author.orcid0000-0001-7217-5705-
Appears in Collections:FCTUC Matemática - Artigos em Revistas Internacionais
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