Inferences for uncertain nonparametric regression by least absolute deviations

Jianhua Ding, Hongyu Zhang and Zhiqiang Zhang

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 16, 5640-5649

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.

Date: 2023
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DOI: 10.1080/03610926.2021.2016832

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