Open AccessRobust and efficient estimation for nonlinear model based on composite quantile regression with missing covariates
Author(s) -
Qiang Zhao,
AUTHOR_ID,
Chao Zhang,
Jin Wu,
Xiuli Wang,
AUTHOR_ID
Publication year - 2022
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 15
ISSN - 2473-6988
DOI - 10.3934/math.2022452
Subject(s) - quantile , estimator , covariate , mathematics , quantile regression , asymptotic distribution , missing data , statistics , nonlinear system , econometrics , physics , quantum mechanics
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.