A Deep Inverse Weibull Network

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 has attracted attention in analyzing lifetime data due to its flexibility in handling complex covariates. The networks introduced in the literature have some restrictions such as proportional hazard assumption, data discretization, monotonicity of hazard rates and heavy tailed assumption. In this paper, a novel neural network is introduced based on inverse Weibull distribution and random censoring that removes some of the restrictions introduced in the literature. The network doesn't put monotonicity, proportionality or heavy tailed assumptions on the hazard function. Also, the network doesn't require data discretization. To test its applicability, 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.