A Simple Nonparametric Estimator of the Bivariate Survival Function Under Univariate Censoring
提出一个简单非参数估计量，用于估计单变量删失下配对失效时间的双变量生存函数，该估计量强相合且弱收敛于高斯过程，数值模拟显示在小样本下表现良好。
In the presence of univariate censoring, the bivariate survival function of paired failure times can be expressed as the ratio of the bivariate at-risk probability to the survival function of the censoring time. The use of this natural representation yields a very simple nonparametric estimator for the bivariate survival curve. The estimator is strongly consistent and, upon proper normalization, converges weakly to a zero-mean Gaussian process with an easily estimated covariance function. Numerical studies demonstrate that both the survival curve estimator and its covariance function estimator perform markedly well for practical sample sizes. Applications to the correlation problem and to the interval estimation of the difference in median survival times are also studied.