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• W2065987610 abstract "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." @default.
• W2065987610 created "2016-06-24" @default.
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• W2065987610 date "2014-01-01" @default.
• W2065987610 modified "2023-10-06" @default.
• W2065987610 title "An Introduction to Dimension Reduction in Nonparametric Kernel Regression" @default.
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• W2065987610 doi "https://doi.org/10.1051/eas/1466012" @default.
• W2065987610 hasPublicationYear "2014" @default.
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