Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/52420
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Saavedra Santana, Pedro | en_US |
dc.contributor.author | Hernández, C. N. | en_US |
dc.contributor.author | Artiles, J. | en_US |
dc.date.accessioned | 2018-11-25T20:10:54Z | - |
dc.date.available | 2018-11-25T20:10:54Z | - |
dc.date.issued | 2000 | en_US |
dc.identifier.issn | 0361-0926 | en_US |
dc.identifier.uri | http://hdl.handle.net/10553/52420 | - |
dc.description.abstract | A 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.language | eng | en_US |
dc.relation.ispartof | Communications in Statistics - Theory and Methods | en_US |
dc.source | Communications in Statistics - Theory and Methods [ISSN 0361-0926], v. 29 (11), p. 2343-2362 | en_US |
dc.subject | 240401 Bioestadística | en_US |
dc.subject.other | Average periodogram | en_US |
dc.subject.other | Kernel spectral estimate | en_US |
dc.subject.other | Bandwidth | en_US |
dc.title | Spectral analysis with replicated time series | en_US |
dc.type | info:eu-repo/semantics/Article | es |
dc.type | Article | es |
dc.identifier.doi | 10.1080/03610920008832610 | en_US |
dc.identifier.scopus | 26844453046 | - |
dc.identifier.isi | 000165138300001 | |
dc.contributor.authorscopusid | 56677724200 | - |
dc.contributor.authorscopusid | 8971071000 | - |
dc.contributor.authorscopusid | 8971071100 | - |
dc.identifier.eissn | 1532-415X | - |
dc.description.lastpage | 2362 | - |
dc.identifier.issue | 11 | - |
dc.description.firstpage | 2343 | - |
dc.relation.volume | 29 | - |
dc.investigacion | Ciencias | en_US |
dc.type2 | Artículo | en_US |
dc.contributor.daisngid | 3459270 | |
dc.contributor.daisngid | 5322222 | |
dc.contributor.daisngid | 11928510 | |
dc.contributor.wosstandard | WOS:Saavedra, P | |
dc.contributor.wosstandard | WOS:Hernandez, CN | |
dc.contributor.wosstandard | WOS:Artiles, J | |
dc.date.coverdate | Diciembre 2000 | |
dc.identifier.ulpgc | Sí | es |
dc.description.jcr | 0,193 | |
dc.description.jcrq | Q4 | |
dc.description.scie | SCIE | |
item.fulltext | Sin texto completo | - |
item.grantfulltext | none | - |
crisitem.author.dept | GIR Estadística | - |
crisitem.author.dept | Departamento de Matemáticas | - |
crisitem.author.dept | GIR Estadística | - |
crisitem.author.dept | Departamento de Matemáticas | - |
crisitem.author.dept | GIR Estadística | - |
crisitem.author.orcid | 0000-0003-1681-7165 | - |
crisitem.author.orcid | 0000-0003-0415-822X | - |
crisitem.author.parentorg | Departamento de Matemáticas | - |
crisitem.author.parentorg | Departamento de Matemáticas | - |
crisitem.author.parentorg | Departamento de Matemáticas | - |
crisitem.author.fullName | Saavedra Santana, Pedro | - |
crisitem.author.fullName | Hernández Flores, Carmen Nieves | - |
crisitem.author.fullName | Artiles Romero,Juan | - |
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