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A sliced inverse regression approach for data stream

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierModelling and Inference of Complex and Structured Stochastic Systems [MISTIS]
dc.contributor.authorGIRARD, Stéphane
hal.structure.identifierAménités et dynamiques des espaces ruraux [UR ADBX]
dc.contributor.authorKUENTZ, Vanessa
hal.structure.identifierEpidémiologie et Biostatistique [Bordeaux]
dc.contributor.authorLIQUET, Benoit
hal.structure.identifierInstitut de Recherche Mathématique Avancée [IRMA]
dc.contributor.authorNGUYEN, Thi Mong Ngoc
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierEcole Nationale Supérieure de Cognitique [ENSC]
dc.contributor.authorSARACCO, Jérôme
dc.date.accessioned2024-04-04T02:21:16Z
dc.date.available2024-04-04T02:21:16Z
dc.date.created2013
dc.date.issued2014-10
dc.identifier.issn0943-4062
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/189557
dc.description.abstractEnIn this article, we focus on data arriving sequentially by blocks in a stream. A semiparametric regression model involving a common EDR (Effective Dimension Reduction) direction is assumed in each block. Our goal is to estimate this direction at each arrival of a new block. A simple direct approach consists of pooling all the observed blocks and estimating the EDR direction by the SIR (Sliced Inverse Regression) method. But in practice, some disadvantages appear such as the storage of the blocks and the running time for large sample sizes. To overcome these drawbacks, we propose an adaptive SIR estimator of based on the optimization of a quality measure. The corresponding approach is faster both in terms of computational complexity and running time, and provides data storage benefits. The consistency of our estimator is established and its asymptotic distribution is given. An extension to multiple indices model is proposed. A graphical tool is also provided in order to detect changes in the underlying model, i.e., drift in the EDR direction or aberrant blocks in the data stream. A simulation study illustrates the numerical behavior of our estimator. Finally, an application to real data concerning the estimation of physical properties of the Mars surface is presented.
dc.language.isoen
dc.publisherSpringer Verlag
dc.subject.enEffective dimension reduction (EDR)
dc.subject.enData stream
dc.subject.enSliced inverse regression (SIR)
dc.title.enA sliced inverse regression approach for data stream
dc.typeArticle de revue
dc.identifier.doi10.1007/s00180-014-0483-4
dc.subject.halStatistiques [stat]/Méthodologie [stat.ME]
bordeaux.journalComputational Statistics
bordeaux.page1129-1152
bordeaux.volume29
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue5
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01139870
hal.version4
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01139870v4
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