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Computational outlier detection methods in sliced inverse regression

hal.structure.identifierMéthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]
dc.contributor.authorLORENZO, Hadrien
hal.structure.identifierEcole Nationale Supérieure de Cognitique [ENSC]
hal.structure.identifierMéthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]
dc.contributor.authorSARACCO, Jérôme
dc.date.accessioned2024-04-04T02:45:18Z
dc.date.available2024-04-04T02:45:18Z
dc.date.issued2021-06-15
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191455
dc.description.abstractEnSliced inverse regression (SIR) focuses on the relationship between a dependent variable y and a p-dimensional explanatory variable x in a semiparametric regression model in which the link relies on an index x β and link function f. SIR allows to estimate the direction of β that forms the effective dimension reduction (EDR) space. Based on the estimated index, the link function f can then be nonparametrically estimated using kernel estimator. This two-step approach is sensitive to the presence of outliers in the data. The aim of this paper is to propose computational methods to detect outliers in that kind of single-index regression model. Three outlier detection methods are proposed and their numerical behaviors are illustrated on a simulated sample. To discriminate outliers from "normal" observations, they use IB (in-bags) or OOB (out-of-bags) prediction errors from subsampling or resampling approaches. These methods, implemented in R, are compared with each other in a simulation study. An application on a real data is also provided.
dc.language.isoen
dc.publisherSpringer International Publishing
dc.publisher.locationCham
dc.source.titleAdvances in Contemporary Statistics and Econometrics
dc.title.enComputational outlier detection methods in sliced inverse regression
dc.typeChapitre d'ouvrage
dc.identifier.doi10.1007/978-3-030-73249-3_6
dc.subject.halMathématiques [math]
bordeaux.page101-122
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.title.proceedingAdvances in Contemporary Statistics and Econometrics
hal.identifierhal-03369250
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03369250v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Advances%20in%20Contemporary%20Statistics%20and%20Econometrics&rft.date=2021-06-15&rft.spage=101-122&rft.epage=101-122&rft.au=LORENZO,%20Hadrien&SARACCO,%20J%C3%A9r%C3%B4me&rft.genre=unknown


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