A1017
Title: Smooth backfitting for additive hazard rates
Authors: Stephan Bischofberger - Staburo GmbH (Germany)
Munir Hiabu - University of Copenhagen (Denmark) [presenting]
Enno Mammen - Heidelberg University (Germany)
Jens Perch Nielsen - City, University of London (United Kingdom)
Abstract: Smooth backfitting was first introduced in an additive regression setting via a direct projection alternative to the classic backfitting method in a past study. The original smooth backfitting concept is translated to a survival model considering an additively structured hazard. The model allows for censoring and truncation patterns occurring in many applications such as medical studies or actuarial science. The estimators are shown to be a projection of the data into the space of multivariate hazard functions with smooth additive components. Hence, the hazard estimator is the closest nonparametric additive fit even if the actual hazard rate is not additive. This is different to other additive structure estimators where it is not clear what is being estimated if the model is not true. The full asymptotic theory is provided for the estimators. An implementation of estimators is proposed that shows good performance in practice.
