Penalized Empirical Likelihood-Based Variable Selection for Longitudinal Data Analysis
Sign Up to like & getrecommendations! Published in 2020 at "American Journal of Mathematical and Management Sciences"
DOI: 10.1080/01966324.2020.1837042
Abstract: Abstract Longitudinal data with a large number of covariates have become common in many applications such as epidemiology, clinical research, and therapeutic evaluation. The identification of a sub-model that adequately represents the data are necessary… read more here.
Keywords: longitudinal data; likelihood; empirical likelihood; variable selection ... See more keywords
Penalized empirical likelihood for quantile regression with missing covariates and auxiliary information
Sign Up to like & getrecommendations! Published in 2018 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2017.1335413
Abstract: ABSTRACT Based on the inverse probability weight method, we, in this article, construct the empirical likelihood (EL) and penalized empirical likelihood (PEL) ratios of the parameter in the linear quantile regression model when the covariates… read more here.
Keywords: empirical likelihood; penalized empirical; quantile regression; auxiliary information ... See more keywords