A Deep Inverse Weibull Network

Abstract

Survival analysis is heavily used in different fields like economics, engineering and medicine. The main core of the analysis is to understand the relationship between the covariates and the survival function. The analysis can be performed using traditional statistical models or neural networks. Recently, neural networks have attracted attention in analyzing lifetime data due to its flexibility in handling complex covariates. In this paper, we introduce a new neural network that removes some of the restrictions introduced in the literature. For example, proportional hazard, relative risk, data discretization and monotonic hazards. The network is applied on both simulated and real datasets and the numerical results show that our model outperforms some other methods in the literature.