Induced smoothing for the semiparametric accelerated failure time model: asymptotics and extensions to clustered data
Induced smoothing for the semiparametric accelerated failure time model: asymptotics and extensions to clustered data
复制标题
DOI：
10.1093/biomet/asp025
复制
发表时间：
2009-09-01
期刊：
影响因子：
2.7
通讯作者：
Strawderman, Robert L.
中科院分区：
文献类型：
--
作者：
Johnson, Lynn M.;Strawderman, Robert L.
本文将Brown & Wang（2006）半参数加速失效时间模型的诱导光滑方法推广到了失效时间数据为聚集数据的情况。由此产生的过程允许使用简单且广泛使用的数值方法（例如Newton-Raphson算法）快速准确地计算回归参数估计值和标准误差。回归参数估计被证明是强一致的和渐近正态的，此外，我们证明了平滑估计的渐近分布与不使用平滑得到的一致。这建立了Brown & Wang（2006）对于独立故障时间数据的关键主张，并且还将这样的结果扩展到聚类数据的情况。仿真结果表明，这些平滑的估计执行以及使用最好的方法获得的计算成本的一小部分。
This paper extends the induced smoothing procedure of Brown & Wang (2006) for the semiparametric accelerated failure time model to the case of clustered failure time data. The resulting procedure permits fast and accurate computation of regression parameter estimates and standard errors using simple and widely available numerical methods, such as the Newton-Raphson algorithm. The regression parameter estimates are shown to be strongly consistent and asymptotically normal; in addition, we prove that the asymptotic distribution of the smoothed estimator coincides with that obtained without the use of smoothing. This establishes a key claim of Brown & Wang (2006) for the case of independent failure time data and also extends such results to the case of clustered data. Simulation results show that these smoothed estimates perform as well as those obtained using the best available methods at a fraction of the computational cost.