Neural network modeling often suffers the deficiency of not using a systematic way of improving classical statistical regression models. In this tutorial we exemplify the proposal of the editorial of ASTIN Bulletin 2019/1. We embed a classical generalized linear model into a neural network...

We propose new procedures for estimating the univariate quantities of interest in both additive and multiplicative nonparametric marker dependent hazard models. We work with a full counting process framework that allows for left truncation and right censoring. Our procedures are based on kernels...