Smoothed empirical likelihood for quantile regression models with response data missing at random

Shuanghua Luo (), Changlin Mei and Cheng-yi Zhang
Additional contact information
Shuanghua Luo: Xi’an Jiaotong University
Changlin Mei: Xi’an Jiaotong University
Cheng-yi Zhang: Xi’an Polytechnic University

AStA Advances in Statistical Analysis, 2017, vol. 101, issue 1, No 5, 95-116

Abstract: Abstract This paper studies smoothed quantile linear regression models with response data missing at random. Three smoothed quantile empirical likelihood ratios are proposed first and shown to be asymptotically Chi-squared. Then, the confidence intervals for the regression coefficients are constructed without the estimation of the asymptotic covariance. Furthermore, a class of estimators for the regression parameter is presented to derive its asymptotic distribution. Simulation studies are conducted to assess the finite sample performance. Finally, a real-world data set is analyzed to illustrated the effectiveness of the proposed methods.

Keywords: Quantile regression; Smoothed empirical likelihood; Missing at random; Confidence interval; 62G05; 62G20; 60G42 (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10182-016-0278-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:101:y:2017:i:1:d:10.1007_s10182-016-0278-8

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10182/PS2

DOI: 10.1007/s10182-016-0278-8

Access Statistics for this article

AStA Advances in Statistical Analysis is currently edited by Göran Kauermann and Yarema Okhrin

More articles in AStA Advances in Statistical Analysis from Springer, German Statistical Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().