Robust and efficient estimation for nonlinear model based on composite quantile regression with missing covariates
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2022-02-01
|
| Series: | AIMS Mathematics |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2022452?viewType=HTML |
| Summary: | 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. |
|---|---|
| ISSN: | 2473-6988 |