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Book Sections Year : 2021

Computational outlier detection methods in sliced inverse regression

Abstract

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.
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Dates and versions

hal-03369250 , version 1 (07-10-2021)

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Cite

Hadrien Lorenzo, Jérôme Saracco. Computational outlier detection methods in sliced inverse regression. Advances in Contemporary Statistics and Econometrics, Springer International Publishing, pp.101-122, 2021, ⟨10.1007/978-3-030-73249-3_6⟩. ⟨hal-03369250⟩
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