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

Shuanghua Luo; Changlin Mei; Chengyi Zhang

doi:10.1007/s10182-016-0278-8

ScienceGate Book Chapters

JOURNAL ARTICLE

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

Shuanghua Luo Changlin Mei Chengyi Zhang

Year: 2016 Journal: AStA Advances in Statistical Analysis Vol: 101 (1)Pages: 95-116 Publisher: Springer Science+Business Media

DOI: 10.1007/s10182-016-0278-8

Get Full-Text PDF Get Analytical Report

Keywords:

Mathematics Quantile regression Empirical likelihood Estimator Statistics Quantile Missing data Asymptotic distribution Regression analysis Linear regression Econometrics Applied mathematics

Metrics

Cited By

0.35

FWCI (Field Weighted Citation Impact)

Refs

0.72

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Bayesian Methods and Mixture Models

Physical Sciences → Computer Science → Artificial Intelligence

Statistical Methods and Bayesian Inference

Physical Sciences → Mathematics → Statistics and Probability

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

Metrics

Citation History

Topics

Related Documents

Empirical likelihood for quantile regression models with response data missing at random

Smoothed Empirical Likelihood Inference for Nonlinear Quantile Regression Models with Missing Response

Empirical Likelihood for Composite Quantile Regression Models with Missing Response Data

Smoothed empirical likelihood analysis of partially linear quantile regression models with missing response variables

Quantile regression and its empirical likelihood with missing response at random