What question did this study set out to answer?

To establish efficient and robust estimation methods for nonlinear models subject to missing covariates using weighted composite quantile regression.

January 1, 2022AIMS MathematicsOpen Access

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

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Authors

QZQiang Zhao CZChao Zhang JWJingjing Wu

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Overview

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

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.

Cite This Study

Zhao et al. (2022) studied this question.

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

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January 1, 2022AIMS MathematicsOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

QZQiang Zhao CZChao Zhang JWJingjing Wu

Discussion

Member takes

Overview

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

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.

Cite This Study

Zhao et al. (2022) studied this question.

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

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