Neural Networks for Censored Expectile Regression Based on Data Augmentation

TL;DR

This paper introduces DAERNN, a data augmentation neural network method for censored expectile regression, which effectively models complex, heterogeneous censored data with minimal assumptions and broad applicability.

Contribution

The paper proposes a novel data augmentation based neural network approach for censored expectile regression, addressing limitations of existing methods and improving flexibility and performance.

Findings

DAERNN outperforms existing censored ERNN methods in simulations.

DAERNN achieves predictive accuracy comparable to models with fully observed data.

The approach handles various censoring mechanisms without explicit parametric assumptions.

Abstract

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.