A kernel smoothed semiparametric survival model
A kernel smoothed semiparametric survival model
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DOI：
10.1016/s0378-3758(00)00314-1
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发表时间：
2001-10-01
影响因子：
0.9
通讯作者：
Desmond, AF
中科院分区：
文献类型：
--
作者：
Lu, XW;Singh, RS;Desmond, AF
在具有灵活协变量效应的半参数生存模型中，我们假设基线风险函数是参数化的，而与协变量相关的风险函数则以半参数方式建模。考虑参数的最大广义轮廓似然估计器。我们证明了所得的估计量是 n 根一致的、渐近正态的且有效的。还给出了非参数风险函数的估计量，并推导了其渐近性质。其收敛速度与非参数回归的收敛速度相当。提出了对小鼠白血病数据的应用来说明所提出的方法。皇冠版权所有 (C) 2001 由 Elsevier Science B.V. 出版。保留所有权利。
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. Crown Copyright (C) 2001 Published by Elsevier Science B.V. All rights reserved.