Quantile regression methods with varying-coefficient models for censored data

2015 ◽  
Vol 88 ◽  
pp. 154-172 ◽  
Author(s):  
Shangyu Xie ◽  
Alan T.K. Wan ◽  
Yong Zhou
Keyword(s):  
Quantile Regression ◽  
Censored Data ◽  
Varying Coefficient Models ◽  
Varying Coefficient ◽  
Regression Methods

scholarly journals Smooth varying-coefficient models in Stata

2020 ◽  
Vol 20 (3) ◽  
pp. 647-679
Author(s):  
Fernando Rios-Avila
Keyword(s):  
Computational Cost ◽  
Bandwidth Selection ◽  
Varying Coefficient Models ◽  
Varying Coefficient ◽  
Regression Methods ◽  
Nonparametric Models ◽  
Functional Forms ◽  
Potential Benefits ◽  
Standard Models ◽  
High Computational Cost

Nonparametric regressions are powerful statistical tools that can be used to model relationships between dependent and independent variables with minimal assumptions on the underlying functional forms. Despite their potential benefits, these models have two weaknesses: The added flexibility creates a curse of dimensionality, and procedures available for model selection, like crossvalidation, have a high computational cost in samples with even moderate sizes. An alternative to fully nonparametric models is semiparametric models that combine the flexibility of nonparametric regressions with the structure of standard models. In this article, I describe the estimation of a particular type of semiparametric model known as the smooth varying-coefficient model (Hastie and Tibshirani, 1993, Journal of the Royal Statistical Society, Series B 55: 757–796), based on kernel regression methods, using a new set of commands within vc_pack. These commands aim to facilitate bandwidth selection and model estimation as well as create visualizations of the results.

scholarly journals Penalized spline estimation in varying coefficient models with censored data

Test ◽  
2017 ◽  
Vol 27 (4) ◽  
pp. 871-895
Author(s):  
K. Hendrickx ◽  
P. Janssen ◽  
A. Verhasselt
Keyword(s):  
Censored Data ◽  
Varying Coefficient Models ◽  
Varying Coefficient ◽  
Penalized Spline ◽  
Spline Estimation ◽  
Penalized Spline Estimation

scholarly journals Quantile regression in varying-coefficient models: non-crossing quantile curves and heteroscedasticity

2016 ◽  
Vol 59 (4) ◽  
pp. 1589-1621 ◽  
Author(s):  
Y. Andriyana ◽  
I. Gijbels ◽  
A. Verhasselt
Keyword(s):  
Quantile Regression ◽  
Varying Coefficient Models ◽  
Varying Coefficient

Quantile regression methods for left-truncated and right-censored data

2015 ◽  
Vol 86 (3) ◽  
pp. 443-459 ◽  
Author(s):  
Jung-Yu Cheng ◽  
Shu-Chun Huang ◽  
Shinn-Jia Tzeng
Keyword(s):  
Quantile Regression ◽  
Censored Data ◽  
Right Censored Data ◽  
Regression Methods

scholarly journals DGQR estimation for interval censored quantile regression with varying-coefficient models

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0240046
Author(s):  
ChunJing Li ◽  
Yun Li ◽  
Xue Ding ◽  
XiaoGang Dong
Keyword(s):  
Quantile Regression ◽  
Estimation Method ◽  
Real Data ◽  
Regression Estimation ◽  
Varying Coefficient Models ◽  
Simulation Studies ◽  
Varying Coefficient ◽  
Censored Quantile Regression ◽  
Direct Generalization ◽  
Interval Censored

This paper propose a direct generalization quantile regression estimation method (DGQR estimation) for quantile regression with varying-coefficient models with interval censored data, which is a direct generalization for complete observed data. The consistency and asymptotic normality properties of the estimators are obtained. The proposed method has the advantage that does not require the censoring vectors to be identically distributed. The effectiveness of the method is verified by some simulation studies and a real data example.

Sign in / Sign up

Export Citation Format

Share Document