GIRARD, Stéphane; SARACCO, Jerôme

doi:10.1051/eas/1466012

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GIRARD, Stéphane
Modelling and Inference of Complex and Structured Stochastic Systems [MISTIS]

SARACCO, Jerôme
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Ecole Nationale Supérieure de Cognitique [ENSC]

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Regression methods for astrophysics, Regression methods for astrophysics. 2014, vol. 66, p. 167-196

EDP Sciences

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Nonparametric regression is a powerful tool to estimate nonlinear relations between some predictors and a response variable. However, when the number of predictors is high, nonparametric estimators may suffer from the curse of dimensionality. In this chapter, we show how a dimension reduction method (namely Sliced Inverse Regression) can be combined with nonparametric kernel regression to overcome this drawback. The methods are illustrated both on simulated datasets as well as on an astronomy dataset using the R software.< Leer menos

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An introduction to dimension reduction in nonparametric kernel regression

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