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Statistical inference on uncertain nonparametric regression model

Author

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  • Jianhua Ding

    (Shanxi Datong University)

  • Zhiqiang Zhang

    (Shanxi Datong University)

Abstract

Nonparametric regression analysis is a useful method to explore the relationships among the variables when a parametric form is not known. Assuming the observations of the model are imprecise and modeling the observed data via uncertain variables, this paper proposes least squares estimation of uncertain nonparametric regression model to explore the functional relationships between response variable and explanatory variable. In particular, we employ B-Splines and local polynomials to approximate the nonparametric function, respectively. Estimation of unknown function can be obtained as a solution of least squares and quadratic programming algorithm can be used to compute efficiently the estimator. Numerical examples are given to illustrate the proposed methods.

Suggested Citation

  • Jianhua Ding & Zhiqiang Zhang, 2021. "Statistical inference on uncertain nonparametric regression model," Fuzzy Optimization and Decision Making, Springer, vol. 20(4), pages 451-469, December.
    • Handle: RePEc:spr:fuzodm:v:20:y:2021:i:4:d:10.1007_s10700-021-09353-0
    DOI: 10.1007/s10700-021-09353-0
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    References listed on IDEAS

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    1. Waichon Lio & Baoding Liu, 2018. "Uncertain data envelopment analysis with imprecisely observed inputs and outputs," Fuzzy Optimization and Decision Making, Springer, vol. 17(3), pages 357-373, September.
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    Cited by:

    1. Tingqing Ye & Baoding Liu, 2022. "Uncertain hypothesis test with application to uncertain regression analysis," Fuzzy Optimization and Decision Making, Springer, vol. 21(2), pages 157-174, June.

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