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A quantile-slicing approach for sufficient dimension reduction with censored responses
Hyungwoo Kim1, Seung Jun Shin1
1Department of Statistics, Korea University, Seongbuk-gu, Seoul, South Korea.
Biometrical Journal. Biometrische Zeitschrift
|September 10, 2020
Summary
This study introduces a novel algorithm for sufficient dimension reduction (SDR) in regression with censored data. The method effectively handles censored responses using a quantile-slicing approach, showing promising results in simulations and real-world applications.
Area of Science:
- Statistics
- High-dimensional data analysis
- Survival analysis
Background:
- Sufficient dimension reduction (SDR) is crucial for high-dimensional regression.
- Existing SDR methods often fail with censored data.
- Censored responses pose significant challenges in statistical modeling.
Purpose of the Study:
- To develop a new algorithm for SDR with censored responses.
- To adapt the quantile-slicing scheme for survival data.
- To improve dimension reduction techniques in the presence of censoring.
Main Methods:
- Utilized a quantile-slicing scheme based on recent advancements.
- Estimated the conditional quantile function of true survival time using censored kernel quantile regression.
- Sliced data based on estimated censored regression quantiles, not direct responses.
Main Results:
- The proposed method demonstrates effective dimension reduction for censored data.
- Simulated data analysis confirmed the algorithm's promising performance.
- Real-world data analysis validated the practical utility of the new SDR approach.
Conclusions:
- The novel algorithm successfully performs SDR with censored responses.
- The quantile-slicing approach is effective for handling censored survival data.
- This method offers a valuable tool for high-dimensional regression with censored outcomes.
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