基于数据增强的删失期望回归神经网络
Neural Networks for Censored Expectile Regression Based on Data Augmentation
AI总结：
本文提出基于数据增强的删失期望回归神经网络DAERNN，用于建模异质性删失数据，无需显式参数设定，性能优于现有方法且接近完全数据模型。
摘要：
期望回归神经网络（ERNNs）是捕捉数据异质性和复杂非线性结构的强大工具。然而，现有研究大多集中于完全观测数据，对涉及删失观测的场景关注有限。本文提出一种基于数据增强的ERNNs算法，称为DAERNN，用于对异质性删失数据建模。所提出的DAERNN完全由数据驱动，所需假设极少，且具有相当大的灵活性。模拟研究和实际数据应用表明，DAERNN优于现有的删失ERNNs方法，并达到与在完全观测数据上训练的模型相当的预测性能。此外，该算法提供了一个统一框架，可处理各种删失机制，无需显式参数模型设定，从而增强了其在实际删失数据分析中的适用性。
英文摘要：
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.
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