Nonparametric Regression in Proportional Hazards Models
Copyright policy )Nonparametric Regression in Proportional Hazards Models
Summary: \textit{J. Fan} et al. [Ann. Stat. 25, No. 4, 1661--1690 (1997; Zbl 0890.62023)] considered two kinds of nonparametric estimators of the effects of the covariates in proportional hazards models. One of them has no parametric assumption on the baseline hazard function and is based on the integration of the estimated first order derivative of the regression function. We study the asymptotic properties of the estimator and consider another nonparametric estimator of the effects of the covariates in proportional hazards models. We show both of the estimators have very similar asymptotic properties. The latter is closely related to estimation in two-sample problems and is much easier to calculate.
- Hitotsubashi University Japan
proportional hazards models, integration estimator, Asymptotic distribution theory in statistics, integration, local partial likelihood, censoring time, failure time, two-sample problem, estimation of first-order derivative, Asymptotic properties of nonparametric inference, Nonparametric regression and quantile regression, local polynomial fitting
proportional hazards models, integration estimator, Asymptotic distribution theory in statistics, integration, local partial likelihood, censoring time, failure time, two-sample problem, estimation of first-order derivative, Asymptotic properties of nonparametric inference, Nonparametric regression and quantile regression, local polynomial fitting
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