A kernel smoothed semiparametric survival model

Abstract In a semiparametric survival model with a flexible covariate effect, we suppose the baseline hazard function is parameterized, while the risk function associated with covariates is modeled in a semiparametric way. A maximum generalized profile likelihood estimator for the parameters is considered. We show that the resulting estimator is root- n consistent, asymptotically normal and efficient. An estimator for the nonparametric risk function is also given and its asymptotic properties are derived. Its rate of convergence is shown to be comparable to that in nonparametric regression. An application to mouse leukemia data is presented to illustrate the proposed method.

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