Estimating multiplicative and additive hazard functions by kernel methods
Open Access
- 1 April 2003
- journal article
- Published by Institute of Mathematical Statistics in The Annals of Statistics
- Vol. 31 (2) , 464-492
- https://doi.org/10.1214/aos/1051027877
Abstract
We propose new procedures for estimating the component functions 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 and time-varying covariates. Our procedures are based on kernel hazard estimation as developed by Nielsen and Linton and on the idea of marginal integration. We provide a central limit theorem for the marginal integration estimator. We then define estimators based on finite-step backfitting in both additive and multiplicative cases and prove that these estimators are asymptotically normal and have smaller variance than the marginal integration method.Keywords
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