Empirical likelihood based heteroscedasticity diagnostics for varying coefficient partially nonlinear models

Cuiping Wang; Xiaoshuang Zhou; Peixin Zhao; Cuiping Wang; Xiaoshuang Zhou; Peixin Zhao

doi:10.3934/math.20241652

AIMS Mathematics

2024, Volume 9, Issue 12: 34705-34719. doi: 10.3934/math.20241652

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Research article

Empirical likelihood based heteroscedasticity diagnostics for varying coefficient partially nonlinear models

1.
School of Mathematics and Statistics, Shandong University of Technology, Zibo 255022, China
2.
College of Mathematics and Big Data, Dezhou University, Dezhou 253023, China
3.
College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China

Received: 26 September 2024 Revised: 04 November 2024 Accepted: 06 December 2024 Published: 12 December 2024
MSC : 62G05, 62G20, 62H15

Heteroscedasticity diagnostics of error variance is essential before performing some statistical inference work. This paper is concerned with the statistical diagnostics for the varying coefficient partially nonlinear model. We propose a novel diagnostic approach for heteroscedasticity of error variance in the model by combining it with the empirical likelihood method. Under some mild conditions, the nonparametric version of the Wilks theorem is obtained. Furthermore, simulation studies and a real data analysis are implemented to evaluate the performances of our proposed approaches.
- empirical likelihood,
- heteroscedasticity diagnostics,
- hypothesis test,
- varying coefficient partially nonlinear model
Citation: Cuiping Wang, Xiaoshuang Zhou, Peixin Zhao. Empirical likelihood based heteroscedasticity diagnostics for varying coefficient partially nonlinear models[J]. AIMS Mathematics, 2024, 9(12): 34705-34719. doi: 10.3934/math.20241652

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Abstract

Heteroscedasticity diagnostics of error variance is essential before performing some statistical inference work. This paper is concerned with the statistical diagnostics for the varying coefficient partially nonlinear model. We propose a novel diagnostic approach for heteroscedasticity of error variance in the model by combining it with the empirical likelihood method. Under some mild conditions, the nonparametric version of the Wilks theorem is obtained. Furthermore, simulation studies and a real data analysis are implemented to evaluate the performances of our proposed approaches.

References

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