Related Experiment Video
Updated: Dec 1, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
DGQR estimation for interval censored quantile regression with varying-coefficient models
ChunJing Li1, Yun Li1, Xue Ding1
1School of Mathematics and Statistics, Changchun University of Technology, Changchun, China.
Abstract:
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.
Related Concept Videos
Censoring Survival Data
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Distributions to Estimate Population Parameter
Interpretation of Confidence Intervals
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Quadratic Models

