Robust empirical likelihood for partially linear models via weighted composite quantile regression

Peixin Zhao; Xiaoshuang Zhou

doi:10.1007/s00180-018-0793-z

ScienceGate Book Chapters

JOURNAL ARTICLE

Robust empirical likelihood for partially linear models via weighted composite quantile regression

Peixin Zhao Xiaoshuang Zhou

Year: 2018 Journal: Computational Statistics Vol: 33 (2)Pages: 659-674 Publisher: Springer Science+Business Media

DOI: 10.1007/s00180-018-0793-z

Get Full-Text PDF Get Analytical Report

Keywords:

Quantile regression Mathematics Empirical likelihood Quantile Econometrics Linear regression Statistics Quasi-maximum likelihood Linear model Computer science Applied mathematics Maximum likelihood Likelihood function

Metrics

Cited By

0.31

FWCI (Field Weighted Citation Impact)

Refs

0.53

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Advanced Statistical Methods and Models

Physical Sciences → Mathematics → Statistics and Probability

Advanced Statistical Process Monitoring

Social Sciences → Decision Sciences → Statistics, Probability and Uncertainty

Robust empirical likelihood for partially linear models via weighted composite quantile regression

Metrics

Citation History

Topics

Related Documents

Composite quantile regression based robust empirical likelihood for partially linear spatial autoregressive models

Empirical likelihood weighted composite quantile regression with partially missing covariates

Weighted composite quantile regression method via empirical likelihood for non linear models

Weighted Composite Quantile Regression for Heteroscedastic Partially Linear Models

Weighted composite quantile regression for partially linear varying coefficient models