  Smooth backfitting has proven to have a number of theoretical and practical
advantages in structured regression. Smooth backfitting projects the data down
onto the structured space of interest providing a direct link between data and
estimator. This paper introduces the ideas of smooth backfitting to survival
analysis in a proportional hazard model, where we assume an underlying
conditional hazard with multiplicative components. We develop asymptotic theory
for the estimator and we use the smooth backfitter in a practical application,
where we extend recent advances of in-sample forecasting methodology by
allowing more information to be incorporated, while still obeying the
structured requirements of in-sample forecasting.
