Neural Networks for Censored Expectile Regression Based on Data Augmentation
This work addresses censored data analysis for researchers and practitioners, offering a flexible, assumption-light method, but it is incremental as it builds on existing ERNNs with a new augmentation approach.
The paper tackled the problem of modeling heterogeneous censored data with expectile regression neural networks (ERNNs) by proposing a data augmentation algorithm (DAERNN), which outperformed existing censored ERNNs methods and achieved predictive performance comparable to models trained on fully observed data.
Expectile regression neural networks (ERNNs) are powerful tools for capturing heterogeneity and complex nonlinear structures in data. However, most existing research has primarily focused on fully observed data, with limited attention paid to scenarios involving censored observations. In this paper, we propose a data augmentation based ERNNs algorithm, termed DAERNN, for modeling heterogeneous censored data. The proposed DAERNN is fully data driven, requires minimal assumptions, and offers substantial flexibility. Simulation studies and real data applications demonstrate that DAERNN outperforms existing censored ERNNs methods and achieves predictive performance comparable to models trained on fully observed data. Moreover, the algorithm provides a unified framework for handling various censoring mechanisms without requiring explicit parametric model specification, thereby enhancing its applicability to practical censored data analysis.