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

Qiang Zhao; Chao Zhang; Jingjing Wu; Xiuli Wang

doi:10.3934/math.2022452

January 1, 2022AIMS MathematicsOpen Access

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

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Key Points

To establish efficient and robust estimation methods for nonlinear models subject to missing covariates using weighted composite quantile regression.
Constructed two types of weighted quantile estimators and established their asymptotic normality within nonlinear models containing missing covariate data.
Derived optimal weight allocations to obtain asymptotic distributions and tested performance via numerical simulations alongside an empirical real-data application.
Derived the theoretical asymptotic distributions of the proposed optimal weighted quantile average estimators under standard regularity conditions.
Demonstrated through finite-sample simulations and real-world data analysis that the proposed estimators achieve superior estimation accuracy and robustness over competing models.

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Implication

Methodological evaluation demonstrates robust parameter estimation in nonlinear models with missing covariates, indicating improved efficiency through optimal weighted quantile regression.

Abstract

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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Zhao et al. (2022) studied this question.

synapsesocial.com/papers/6a0ea0a406ecbe833447a14d https://doi.org/10.3934/math.2022452

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

Key Points

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Implication

Abstract

Discussion

Cite This Study

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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