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http://dx.doi.org/10.5351/KJAS.2021.34.6.957

A comparison study of inverse censoring probability weighting in censored regression  

Shin, Jungmin (Department of Mathematics, Korea Military Academy)
Kim, Hyungwoo (Department of Statistics, Korea University)
Shin, Seung Jun (Department of Statistics, Korea University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.6, 2021 , pp. 957-968 More about this Journal
Abstract
Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.
Keywords
censored regression; censoring probability estimation; dimension reduction; cox proportional hazard model; local Kaplan-Meier estimator;
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