Facultade de Fisioterapia

Penalized empirical likelihood for quantile regression with missing covariates and auxiliary information

Shen, Yu; Liang, Han-Ying; Fan, Guo-Liang
Abstract:
Based on the inverse probability weight method, we, in this article, construct the empirical likelihood (EL) and penalized empirical likelihood (PEL) ratios of the parameter in the linear quantile regression model when the covariates are missing at random, in the presence and absence of auxiliary information, respectively. It is proved that the EL ratio admits a limiting Chi-square distribution. At the same time, the asymptotic normality of the maximum EL and PEL estimators of the parameter is established. Also, the variable selection of the model in the presence and absence of auxiliary information, respectively, is discussed. Simulation study and a real data analysis are done to evaluate the performance of the proposed methods.
Year:
2018
Type of Publication:
Article
Keywords:
Auxiliary information; Missing at random; Penalized empirical likelihood; Quantile regression; Variable selection; GENERAL ESTIMATING EQUATIONS; VARIABLE SELECTION; ORACLE PROPERTIES; LINEAR-MODELS; LASSO
Journal:
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
Volume:
47
Number:
8
Pages:
2001-2021
DOI:
10.1080/03610926.2017.1335413
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