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Variance Estimation in Censored Quantile Regression via Induced Smoothing
Lei Panga1, Wenbin Lu, Huixia Judy Wang
1Department of Statistics, North Carolina State University, Raleigh, NC 27606, U.S.A.
Computational Statistics & Data Analysis
|May 2, 2012
Summary
This study introduces a new method for estimating variance in censored quantile regression. The induced smoothing technique improves computational efficiency and accuracy for statistical inference in survival analysis.
Area of Science:
- Statistics
- Biostatistics
- Survival Analysis
Background:
- Statistical inference in censored quantile regression presents challenges due to the quantile score function's unsmoothness.
- Existing methods may lack computational efficiency or accuracy.
Purpose of the Study:
- To develop a novel procedure for estimating the variance of the inverse-censoring-probability weighted estimator in censored quantile regression.
- To address the challenges posed by unsmoothness in quantile score functions.
Main Methods:
- Employing the concept of induced smoothing to estimate variance.
- Developing a new procedure for censored quantile regression analysis.
Main Results:
- The proposed variance estimator is asymptotically consistent.
- Numerical studies indicate good performance in finite samples.
- The new procedure is computationally more efficient than the bootstrap method.
Conclusions:
- The induced smoothing approach offers a robust and efficient solution for variance estimation in censored quantile regression.
- This method enhances statistical inference for survival data analysis.
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