Bootstrapping confidence intervals for the change-point of time series

dc.creatorHuskova, Marie
dc.creatorKirch, Claudia
dc.date2007-06-11
dc.date.accessioned2026-07-07T10:13:43Z
dc.date.available2026-07-07T10:13:43Z
dc.descriptionWe study an AMOC time series model with an abrupt change in the mean and dependent errors that fulfill certain mixing conditions. We obtain confidence intervals for the unknown change-point via bootstrapping methods. Precisely we use a block bootstrap of the estimated centered error sequence. Then we reconstruct a sequence with a change in the mean using the same estimators as before. The difference between the change-point estimator of the resampled sequence and the one for the original sequence can be use as an approximation of the difference between the real change-point and its estimator. This enables us to construct confidence intervals using the empirical distribution of the resampled time series. A simulation study shows that the resampled confidence intervals are usually closer to their target levels and at the same time smaller than the asymptotic intervals.
dc.description25 pages, 25 figures
dc.identifierhttps://arxiv.org/abs/0706.1485
dc.identifierhttp://arxiv.org/abs/0706.1485
dc.identifierJournal of Time Series Analysis. 29:947-972, 2008
dc.identifierdoi:10.1111/j.1467-9892.2008.00589.x
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/172628
dc.subjectStatistics Theory
dc.subject62G09, 62G15, 60G10
dc.titleBootstrapping confidence intervals for the change-point of time series
dc.typetext

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