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Data-driven resistant kernel regression
Journal article   Peer reviewed

Data-driven resistant kernel regression

Jianhua Zhou and Christopher F. Parmeter
Journal of nonparametric statistics
2024-04-04

Abstract

Mathematics Physical Sciences Science & Technology Statistics & Probability
We investigate data-driven bandwidth selection within the confines of robust (resistant) kernel smoothing. While several approaches presently exist, they require user defined robustness parameters. We discuss identification issues within this setting and propose several tractable avenues to fully operationalise this approach. Simulations reveal that the proposed selection methods perform well relative to competing approaches and a small empirical example illustrates its usefulness.

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