Work

Empirical Likelihood for Composite Quantile Regression Models with Missing Response Data

Year: 2024

Type: article

Abstract: Under the assumption of missing response data, empirical likelihood inference is studied via composite quantile regression. Firstly, three empirical likelihood ratios of composite quantile regression are given and proved to be asymptotically χ2. Secondly, without an estimation of the asymptotic covariance, confidence intervals are constructed for the regression coefficients. Thirdly, three estimators are presented for the regression parameters to obtain its asymptotic distribution. The finite sample performance is assessed through simulation studies, and the symmetry confidence intervals of the parametric are constructed. Finally, the effectiveness of the proposed methods is illustrated by analyzing a real-world data set.

Source: Symmetry

DOI: 10.3390/sym16101314

Authors: Shuanghua Luo, Yu Zheng, Cheng-yi Zhang

Institutions: Xi'an Polytechnic University, Xi'an Jiaotong University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funders: National Natural Science Foundation of China, Natural Science Foundation of Shaanxi Province