Robust Nonparametric Regression for Heavy-Tailed Data

Ferdos Gorji () and Mina Aminghafari ()
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Ferdos Gorji: Amirkabir University of Technology
Mina Aminghafari: Amirkabir University of Technology

Journal of Agricultural, Biological and Environmental Statistics, 2020, vol. 25, issue 3, No 1, 277-291

Abstract: Abstract We propose a robust nonparametric regression method that can deal with heavy-tailed noise and also a heavy-tailed input variable. We decompose the trajectory matrix of the response variable of the regression problem to extract the regression function in a nonparametric way. We implement the decomposition in a robust way using iterative robust linear regressions. We show the effectiveness of the proposed method on synthetic and real data in comparison with two other nonparametric methods and a robust linear method.

Keywords: Robust nonparametric regression; Matrix decomposition; Heavy-tailed data (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s13253-019-00382-2

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