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Inferences for uncertain nonparametric regression by least absolute deviations

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  • Jianhua Ding
  • Hongyu Zhang
  • Zhiqiang Zhang

Abstract

The observations of some samples are usually collected in an imprecise way. By employing uncertain variables to model these imprecise observations, this paper proposes uncertain statistical inferences for nonparametric regression model based on the least absolute deviations criterion. A numerical example and simulation comparison with least squares estimate are presented to illustrate the proposed method.

Suggested Citation

  • Jianhua Ding & Hongyu Zhang & Zhiqiang Zhang, 2023. "Inferences for uncertain nonparametric regression by least absolute deviations," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(16), pages 5640-5649, August.
  • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:16:p:5640-5649
    DOI: 10.1080/03610926.2021.2016832
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