Weighted composite quantile regression method via empirical likelihood for non linear models. Issue 17 (2nd September 2018)

• Record Type: Journal Article
• Authors: Li, Yunxia; Ding, Jiali
• Is Part Of: Communications in statistics. Volume 47:Issue 17(2018)
• Page Start: 4286
• Page End: 4296
• Publication Date: 2018-09-02
• Subjects: Empirical likelihood -- logistic model -- non linear model -- weighted composite quantile regression; 62G15 -- 62G20; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2017.1373816
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: In this paper, we investigate empirical likelihood (EL) inferences via weighted composite quantile regression for non linear models. Under regularity conditions, we establish that the proposed empirical log-likelihood ratio is asymptotically chi-squared, and then the confidence intervals for the regression coefficients are constructed. The proposed method avoids estimating the unknown error density function involved in the asymptotic covariance matrix of the estimators. Simulations suggest that the proposed EL procedure is more efficient and robust, and a real data analysis is used to illustrate the performance.