Efficient estimation in the partially linear quantile regression model for longitudinal data
type: Item
instance of: scholarly article
title: Efficient estimation in the partially linear quantile regression model for longitudinal data
zbMATH Open document ID: 1401.62061
DOI: 10.1214/18-EJS1409
author: Hyunkeun Ryan Cho
author: Seon Jin Kim
published in: Electronic Journal of Statistics
publication date: 2018-04-25T00:00:00Z
full work available at URL: https://projecteuclid.org/euclid.ejs/1520046230
review text: The authors study efficient estimation for a quantile regression model with partially linear coefficients for longitudinal data where repeated measurements within each subject are likely to be correlated. They propose a weighted quantile approach for time-invariant and time varying coefficient estimation. The method is illustrated through a Multi-Center AIDS Cohort study.
reviewed by: B. L. S. Prakasa Rao
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 6864478
zbMATH Keywords: empirical likelihood
zbMATH Keywords: kernel smoothing
zbMATH Keywords: quantile regression
zbMATH Keywords: quadratic inference function
zbMATH Keywords: semiparametric regression
MaRDI profile type: Publication
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OpenAlex ID: W2790004130
description: scientific article; zbMATH DE number 6864478