Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/52420
DC FieldValueLanguage
dc.contributor.authorSaavedra Santana, Pedroen_US
dc.contributor.authorHernández, C. N.en_US
dc.contributor.authorArtiles, J.en_US
dc.date.accessioned2018-11-25T20:10:54Z-
dc.date.available2018-11-25T20:10:54Z-
dc.date.issued2000en_US
dc.identifier.issn0361-0926en_US
dc.identifier.urihttp://hdl.handle.net/10553/52420-
dc.description.abstractA doubly stochastic process {x(b,t);b∊B,t∊Z} is considered, with (B,β,Pβ) being a probability space so that for each b, {X(b,t);t ∊ Z} is a stationary process with an absolutely continuous spectral distribution. The population spectrum is defined as f(ω) = EB[Q(b,ω)] with Q(b,ω) being the spectral density function of X(b,t). The aim of this paper is to estimate f(ω) by means of a random sample b1,…,br from (B,β,Pβ). For each b1∊ B, the processes X(b1,t) are observed at the same times t=1,…,N. Thus, r time series (x(b1,t)} are available in order to estimate f(ω). A model for each individual periodogram, which involves f(ω), is formulated. It has been proven that a certain family of linear stationary processes follows the above model In this context, a kernel estimator is proposed in order to estimate f(ω). The bias, variance and asymptotic distribution of this estimator are investigated under certain conditions.en_US
dc.languageengen_US
dc.relation.ispartofCommunications in Statistics - Theory and Methodsen_US
dc.sourceCommunications in Statistics - Theory and Methods [ISSN 0361-0926], v. 29 (11), p. 2343-2362en_US
dc.subject240401 Bioestadísticaen_US
dc.subject.otherAverage periodogramen_US
dc.subject.otherKernel spectral estimateen_US
dc.subject.otherBandwidthen_US
dc.titleSpectral analysis with replicated time seriesen_US
dc.typeinfo:eu-repo/semantics/Articlees
dc.typeArticlees
dc.identifier.doi10.1080/03610920008832610en_US
dc.identifier.scopus26844453046-
dc.identifier.isi000165138300001
dc.contributor.authorscopusid56677724200-
dc.contributor.authorscopusid8971071000-
dc.contributor.authorscopusid8971071100-
dc.identifier.eissn1532-415X-
dc.description.lastpage2362-
dc.identifier.issue11-
dc.description.firstpage2343-
dc.relation.volume29-
dc.investigacionCienciasen_US
dc.type2Artículoen_US
dc.contributor.daisngid3459270
dc.contributor.daisngid5322222
dc.contributor.daisngid11928510
dc.contributor.wosstandardWOS:Saavedra, P
dc.contributor.wosstandardWOS:Hernandez, CN
dc.contributor.wosstandardWOS:Artiles, J
dc.date.coverdateDiciembre 2000
dc.identifier.ulpgces
dc.description.jcr0,193
dc.description.jcrqQ4
dc.description.scieSCIE
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptGIR Estadística-
crisitem.author.deptDepartamento de Matemáticas-
crisitem.author.deptGIR Estadística-
crisitem.author.deptDepartamento de Matemáticas-
crisitem.author.deptGIR Estadística-
crisitem.author.orcid0000-0003-1681-7165-
crisitem.author.orcid0000-0003-0415-822X-
crisitem.author.parentorgDepartamento de Matemáticas-
crisitem.author.parentorgDepartamento de Matemáticas-
crisitem.author.parentorgDepartamento de Matemáticas-
crisitem.author.fullNameSaavedra Santana, Pedro-
crisitem.author.fullNameHernández Flores, Carmen Nieves-
crisitem.author.fullNameArtiles Romero,Juan-
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