Quantile estimation in dependent sequences

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dc.contributor.authorHeidelberger, P.
dc.contributor.authorLewis, Peter A. W.
dc.contributor.departmentOperations Research (OR)
dc.date.accessioned2013-03-07T21:53:52Z
dc.date.available2013-03-07T21:53:52Z
dc.date.issued1981-09
dc.description.abstractStandard nonparametric estimators of quantiles based on order statistics can be used not only when the data are i.i.d., but also when the data are from a stationary, phi-mixing process of continuous random variables. However, when the random variables are highly positively correlated, sample sizes needed for acceptable precision in estimates of extreme quantiles are computationally unmanageable. A practical scheme is given, based on a maximum transformation in a two-way layout of the data, which reduces the sample size sufficiently to allow an experimenter to obtain a point estimate of an extreme quantile. Three schemes are then given which lead to confidence interval estimates for the quantile. One uses a spectral analysis of the reduced sample. The other two, averaged group quantiles and nested group quantiles, are extensions of the method of batched means to quantile estimation. None of the schemes requires that the process being simulated is regenerativeen_US
dc.description.funderN0001481WR10001en_US
dc.description.sponsorshipPrepared for: Chief of Naval Research Arlington, VAen_US
dc.description.urihttp://archive.org/details/quantileestimati00heid
dc.identifier.npsreportNPS-55-81-015
dc.identifier.urihttps://hdl.handle.net/10945/30117
dc.publisherMonterey, CA; Naval Postgraduate School
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.subject.authorQuantileen_US
dc.subject.authorquantile estimationen_US
dc.subject.authordependent sequencesen_US
dc.subject.authornonparametric quantile estimatorsen_US
dc.subject.author^-mixing processen_US
dc.subject.authormaximum transformationen_US
dc.subject.authorextreme quantileen_US
dc.subject.authorconfidence interval estimates; averaged group quantilesen_US
dc.subject.authornested group quantiles.en_US
dc.subject.lcshHUMAN-MACHINE SYSTEMS.COMPUTER NETWORKS.en_US
dc.titleQuantile estimation in dependent sequencesen_US
dc.typeTechnical Reporten_US
dspace.entity.typePublication
relation.isDepartmentOfPublication58745961-c46a-45ad-ae9c-d139d1ba1041
relation.isDepartmentOfPublication.latestForDiscovery58745961-c46a-45ad-ae9c-d139d1ba1041
relation.isOrgUnitOfPublication58745961-c46a-45ad-ae9c-d139d1ba1041
relation.isOrgUnitOfPublicationdd7f1b97-9c92-402d-b910-27f080946cde
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