# Neural Networks for Censored Expectile Regression Based on Data Augmentation

arXiv: https://arxiv.org/abs/2510.20344 · HTML: https://mlsanity.com/paper/2510.20344/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 56 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Error bounds for approximations with deep ReLU networks](https://mlsanity.com/paper/1610.01145/) (2016)
- [Benefits of depth in neural networks](https://mlsanity.com/paper/1602.04485/) (2016)
- [An SVM-like Approach for Expectile Regression](https://mlsanity.com/paper/1507.03887/) (2015)
- [Deep Neural Network Based Accelerated Failure Time Models using Rank Loss](https://mlsanity.com/paper/2206.05974/) (2022)
- [Right-censored models by the expectile method](https://mlsanity.com/paper/2402.03203/) (2024)
- [Interval-censored linear quantile regression](https://mlsanity.com/paper/2404.11125/) (2024)

## Abstract (arXiv)

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.