LORENZO, Hadrien; SARACCO, Jérôme

doi:10.1007/978-3-030-73249-3_6

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LORENZO, Hadrien
Méthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]

SARACCO, Jérôme
Ecole Nationale Supérieure de Cognitique [ENSC]
Méthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]

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Advances in Contemporary Statistics and Econometrics, Advances in Contemporary Statistics and Econometrics. 2021-06-15p. 101-122

Springer International Publishing

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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.< Réduire

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Computational outlier detection methods in sliced inverse regression

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