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Computational outlier detection methods in sliced inverse regression
| hal.structure.identifier | Méthodes avancées d’apprentissage statistique et de contrôle [ASTRAL] | |
| dc.contributor.author | LORENZO, Hadrien | |
| hal.structure.identifier | Ecole Nationale Supérieure de Cognitique [ENSC] | |
| hal.structure.identifier | Méthodes avancées d’apprentissage statistique et de contrôle [ASTRAL] | |
| dc.contributor.author | SARACCO, Jérôme | |
| dc.date.accessioned | 2024-04-04T02:45:18Z | |
| dc.date.available | 2024-04-04T02:45:18Z | |
| dc.date.issued | 2021-06-15 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/191455 | |
| dc.description.abstractEn | Sliced 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.iso | en | |
| dc.publisher | Springer International Publishing | |
| dc.publisher.location | Cham | |
| dc.source.title | Advances in Contemporary Statistics and Econometrics | |
| dc.title.en | Computational outlier detection methods in sliced inverse regression | |
| dc.type | Chapitre d'ouvrage | |
| dc.identifier.doi | 10.1007/978-3-030-73249-3_6 | |
| dc.subject.hal | Mathématiques [math] | |
| bordeaux.page | 101-122 | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.title.proceeding | Advances in Contemporary Statistics and Econometrics | |
| hal.identifier | hal-03369250 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-03369250v1 | |
| bordeaux.COinS | ctx_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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