Robust Estimation and Tests for Parameters of Some Nonlinear Regression Models

Pengfei Liu, Mengchen Zhang, Ru Zhang and Qin Zhou
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Mengchen Zhang: Department of Public Administration and Policy, College of Public Affairs, National Taipei University, Taipei 237, Taiwan
Ru Zhang: School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou 221116, China
Qin Zhou: School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou 221116, China

Mathematics, 2021, vol. 9, issue 6, 1-16

Abstract: This paper uses the median-of-means (MOM) method to estimate the parameters of the nonlinear regression models and proves the consistency and asymptotic normality of the MOM estimator. Especially when there are outliers, the MOM estimator is more robust than nonlinear least squares (NLS) estimator and empirical likelihood (EL) estimator. On this basis, we propose hypothesis testing Statistics for the parameters of the nonlinear regression models using empirical likelihood method, and the simulation performance shows the superiority of MOM estimator. We apply the MOM method to analyze the top 50 data of GDP of China in 2019. The result shows that MOM method is more feasible than NLS estimator and EL estimator.

Keywords: median-of-means (MOM); nonlinear regression (NR); empirical likelihood (EL); hypothesis testing (HT) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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