Neural network for censored expectile regression based on data augmentation

Abstract

Expectile regression neural network (ERNN) is powerful tool for capturing heterogeneity and complex nonlinear structures in data. However, most existing research has primarily focused on fully observed data, with reletive limited attention given to scenarios involving censored observations. In this paper, we propose a data-augmentation–based ERNN algorithm, termed DAERNN, for modeling heterogeneous censored data with complex relationships among variables. The proposed DAERNN is flexible and capable of exploring potential nonlinear effects of covariates on the conditional expectiles of the response under various types of censoring, thereby enhancing its applicability to practical censored data analysis. Moreover, DAERNN can be readily implemented via a data augmentation strategy combined with a standard gradient-based optimization algorithm, which directly yields estimates of the conditional expectile functions. The advantages of the DAERNN are illustrated through extensive Monte Carlo simulation studies and two real data applications. The results show that DAERNN outperforms existing censored ERNN methods as well as other competing approaches.