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Robust Nonparametric Regression for Heavy-Tailed Data

Author

Listed:
  • Ferdos Gorji

    (Amirkabir University of Technology)

  • Mina Aminghafari

    (Amirkabir University of Technology)

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.

Suggested Citation

  • Ferdos Gorji & Mina Aminghafari, 2020. "Robust Nonparametric Regression for Heavy-Tailed Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 277-291, September.
    • Handle: RePEc:spr:jagbes:v:25:y:2020:i:3:d:10.1007_s13253-019-00382-2
    DOI: 10.1007/s13253-019-00382-2
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    References listed on IDEAS

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