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AIMS Mathematics

2022, Volume 7, Issue 5: 8127-8146. doi: 10.3934/math.2022452
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Research article

Robust and efficient estimation for nonlinear model based on composite quantile regression with missing covariates

  • 1.
    School of Mathematics and Statistics, Shandong Normal University, Jinan 250014, China
  • 2.
    Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada
  • Received: 16 November 2021 Revised: 22 January 2022 Accepted: 09 February 2022 Published: 24 February 2022

  • MSC : 62F12, 62G08

  • In this article, two types of weighted quantile estimators were proposed for nonlinear models with missing covariates. The asymptotic normality of the proposed weighted quantile average estimators was established. We further calculated the optimal weights and derived the asymptotic distributions of the correspondingly resulted optimal weighted quantile estimators. Numerical simulations and a real data analysis were conducted to examine the finite sample performance of the proposed estimators compared with other competitors.

    Citation: Qiang Zhao, Chao Zhang, Jingjing Wu, Xiuli Wang. Robust and efficient estimation for nonlinear model based on composite quantile regression with missing covariates[J]. AIMS Mathematics, 2022, 7(5): 8127-8146. doi: 10.3934/math.2022452

    Related Papers:

  • In this article, two types of weighted quantile estimators were proposed for nonlinear models with missing covariates. The asymptotic normality of the proposed weighted quantile average estimators was established. We further calculated the optimal weights and derived the asymptotic distributions of the correspondingly resulted optimal weighted quantile estimators. Numerical simulations and a real data analysis were conducted to examine the finite sample performance of the proposed estimators compared with other competitors.


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