Nonanticipating estimation applied to sequential analysis and changepoint detection

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dc.creatorLorden, Gary
dc.creatorPollak, Moshe
dc.date2005-07-21
dc.date.accessioned2026-07-07T08:07:05Z
dc.date.available2026-07-07T08:07:05Z
dc.descriptionSuppose a process yields independent observations whose distributions belong to a family parameterized by θ\inΘ. When the process is in control, the observations are i.i.d. with a known parameter value θ_0. When the process is out of control, the parameter changes. We apply an idea of Robbins and Siegmund [Proc. Sixth Berkeley Symp. Math. Statist. Probab. 4 (1972) 37-41] to construct a class of sequential tests and detection schemes whereby the unknown post-change parameters are estimated. This approach is especially useful in situations where the parametric space is intricate and mixture-type rules are operationally or conceptually difficult to formulate. We exemplify our approach by applying it to the problem of detecting a change in the shape parameter of a Gamma distribution, in both a univariate and a multivariate setting.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053605000000183 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0507434
dc.identifierhttp://arxiv.org/abs/math/0507434
dc.identifierAnnals of Statistics 2005, Vol. 33, No. 3, 1422-1454
dc.identifierdoi:10.1214/009053605000000183
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130826
dc.subjectStatistics Theory
dc.subject62L10, 62N10, 62F03 (Primary) 62F05, 60K05. (Secondary)
dc.titleNonanticipating estimation applied to sequential analysis and changepoint detection
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