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dc.contributor.authorBarakat, Haroon
dc.contributor.authorNigm, Elsayed
dc.contributor.authorAldallal, Ramy
dc.date.accessioned2016-07-06T13:57:49Z
dc.date.available2016-07-06T13:57:49Z
dc.date.issued2014-12
dc.identifier.citationBarakat, Haroon; Nigm, Elsayed; Aldallal, Ramy. Exact prediction intervals for future current records and record range from any continuous distribution. "SORT", Desembre 2014, vol. 38, núm. 2, p. 251-270.
dc.identifier.issn1696-2281
dc.identifier.urihttp://hdl.handle.net/2117/88562
dc.description.abstractIn this paper, a general method for predicting future lower and upper current records and record range from any arbitrary continuous distribution is proposed. Two pivotal statistics with the same explicit distribution for lower and upper current records are developed to construct prediction intervals for future current records. In addition, prediction intervals for future observations of the record range are constructed. A simulation study is applied on normal and Weibull distributions to investigate the efficiency of the suggested method. Finally, an example for real lifetime data with unknown distribution is analysed.
dc.format.extent20 p.
dc.language.isoeng
dc.publisherInstitut d'Estadística de Catalunya
dc.relation.ispartofSORT. 2014, Vol. 38, Núm. 2
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.otherCurrent record values
dc.subject.otherrecord range
dc.subject.otherpivotal quantity
dc.subject.otherprediction interval
dc.subject.othercoverage probability.
dc.titleExact prediction intervals for future current records and record range from any continuous distribution
dc.typeArticle
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::62 Statistics::62F Parametric inference
dc.subject.amsClassificació AMS::62 Statistics::62G Nonparametric inference
dc.subject.amsClassificació AMS::62 Statistics::62M Inference from stochastic processes
dc.rights.accessOpen Access
local.citation.publicationNameSORT
local.citation.volume38
local.citation.number2
local.citation.startingPage251
local.citation.endingPage270


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