Statistical inference on uncertain nonparametric regression model

Jianhua Ding () and Zhiqiang Zhang ()
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Jianhua Ding: Shanxi Datong University
Zhiqiang Zhang: Shanxi Datong University

Fuzzy Optimization and Decision Making, 2021, vol. 20, issue 4, No 2, 469 pages

Abstract: 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.

Keywords: Uncertain variable; Uncertain nonparametric regression; B-spline; Local polynomial (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10700-021-09353-0

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