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

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• 作者：Shuanghua Luo ; Changlin Mei ; Cheng-yi Zhang
• 关键词：Quantile regression ; Smoothed empirical likelihood ; Missing at random ; Confidence interval
• 刊名：AStA Advances in Statistical Analysis
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：101
• 期：1
• 页码：95-116
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics, general; Statistics for Business/Economics/Mathematical Finance/Insurance; Probability Theory and Stochastic Processes; Econometrics;
• 出版者：Springer Berlin Heidelberg
• ISSN：1863-818X
• 卷排序：101

文摘

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.