Nonparametric Kernel Estimation in Counting Processes with Explanatory Variables
针对Cox比例风险模型，提出一种基于核函数的平滑方法来估计基准风险率，证明估计量的一致性和弱收敛性，并讨论带宽选择问题。
Cox's proportional hazards model assumes that the duration hazard rate factors into a product of a baseline hazard rate and a nonegative function of explanatory variables. An estimate of the baseline hazard rate, hence also of overall hazard rate, is proposed, based on a smoothing procedure using a kernel function. It is shown that the proposed estimator is uniformly consistent and converges weakly to a Gaussian process. Bandwidth selection issues are also discussed.