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Estimating multiplicative and additive hazard functions by kernel methods

By Oliver Linton, J P Nielsen and S van de Geer


We propose new procedures for estimating the component functions in both additive and multiplicativen onparametricm arker-dependenht azard 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

Topics: HB Economic Theory
Publisher: Institute of Mathematical Statistics
Year: 2003
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Provided by: LSE Research Online
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