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Weighted Expectile Regression Neural Networks for Right Censored Data
Feipeng Zhang1, Xi Chen1, Peng Liu2
1School of Economics and Finance, Xi'an Jiaotong University, Xi'an, China.
Statistics in Medicine
|September 29, 2024
Summary
A new weighted expectile regression neural networks (WERNN) method improves survival analysis by flexibly modeling complex covariate effects, outperforming existing methods for censored data.
Area of Science:
- Statistics
- Machine Learning
- Survival Analysis
Background:
- Censored expectile regression is valuable in survival analysis for modeling covariate effects.
- Existing weighted expectile regression (WER) has limitations due to independence and linearity assumptions.
Purpose of the Study:
- To introduce a novel weighted expectile regression neural networks (WERNN) method.
- To overcome the restrictive assumptions of traditional WER methods.
Main Methods:
- Incorporated deep neural networks into the censored expectile regression framework.
- Utilized inverse probability of censoring weighting (IPCW) in the expectile loss function to handle random censoring.
Main Results:
- The proposed WERNN method effectively captures nonlinear covariate effects.
- WERNN demonstrates superior prediction performance compared to the existing WER method for right-censored data.
Conclusions:
- WERNN offers a flexible and accurate approach for survival analysis with censored data.
- The method's performance is validated through simulations and a real-world data application.
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