On the asymptotic theory of subsampling

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dc.contributor.author Politis, Dimitris N.
dc.contributor.author Romano, Joseph P.
dc.contributor.author Wolf, Michael
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2010-01-11T13:17:33Z
dc.date.available 2010-01-11T13:17:33Z
dc.date.issued 1999-06
dc.identifier.uri http://hdl.handle.net/10016/6334
dc.description.abstract A general approach to constructing confidence intervals by subsampling was presented in Politis and Romano (1994). The crux of the method is based on recomputing a statistic over subsamples of the data, and these recomputed values are used to build up an estimated sampling distribution. The method works under extremely weak conditions, it applies to independent, identically distributed (LLd.) observations as well as to dependent data situations, such as time series (possible non stationary) , random fields, and marked point processes. In this article, we present some new theorems showing: a new construction for confidence intervals that removes a previous condition, a general theorem showing the validity of subsampling for datadependent choices of the block size, and a general theorem for the construction of hypothesis tests (which is not necessarily derived from a confidence interval construction). The arguments apply to both the Li.d. setting as well as the dependent data case.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 99-52-17
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Confidence intervals
dc.subject.other Data--dependent block size choice
dc.subject.other Hypothesis tests
dc.subject.other large sample theory
dc.subject.other resampling
dc.title On the asymptotic theory of subsampling
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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