Efficient parameter estimation via modified Cholesky decomposition for quantile regression with longitudinal data

2017 ◽  
Vol 32 (3) ◽  
pp. 947-975
Author(s):  
Jing Lv ◽  
Chaohui Guo
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
I-Chen Chen ◽  
Philip M. Westgate

AbstractWhen observations are correlated, modeling the within-subject correlation structure using quantile regression for longitudinal data can be difficult unless a working independence structure is utilized. Although this approach ensures consistent estimators of the regression coefficients, it may result in less efficient regression parameter estimation when data are highly correlated. Therefore, several marginal quantile regression methods have been proposed to improve parameter estimation. In a longitudinal study some of the covariates may change their values over time, and the topic of time-dependent covariate has not been explored in the marginal quantile literature. As a result, we propose an approach for marginal quantile regression in the presence of time-dependent covariates, which includes a strategy to select a working type of time-dependency. In this manuscript, we demonstrate that our proposed method has the potential to improve power relative to the independence estimating equations approach due to the reduction of mean squared error.


2020 ◽  
Vol 16 (3) ◽  
pp. 1597-1605
Author(s):  
Juan Felipe Huan Lew-Yee ◽  
Roberto Flores-Moreno ◽  
José Luis Morales ◽  
Jorge M. del Campo

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