Weighted quantile regression in varying-coefficient model with longitudinal data

2020 ◽  
Vol 145 ◽  
pp. 106915
Author(s):  
Fangzheng Lin ◽  
Yanlin Tang ◽  
Zhongyi Zhu
2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Yunquan Song ◽  
Ling Jian ◽  
Lu Lin

In this paper, we consider a single-index varying-coefficient model with application to longitudinal data. In order to accommodate the within-group correlation, we apply the block empirical likelihood procedure to longitudinal single-index varying-coefficient model, and prove a nonparametric version of Wilks’ theorem which can be used to construct the block empirical likelihood confidence region with asymptotically correct coverage probability for the parametric component. In comparison with normal approximations, the proposed method does not require a consistent estimator for the asymptotic covariance matrix, making it easier to conduct inference for the model's parametric component. Simulations demonstrate how the proposed method works.


Stat ◽  
2012 ◽  
Vol 1 (1) ◽  
pp. 75-89 ◽  
Author(s):  
Jeng-Min Chiou ◽  
Yanyuan Ma ◽  
Chih-Ling Tsai

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