Distributional Convergence under Random Censorship when Covariables are Present
研究了当存在可观测协变量时，多维Kaplan-Meier估计量的线性泛函在弱矩条件下的渐近正态性，并讨论了在删失相关、回归和平均剩余寿命估计中的应用。
Assume that (Xi, Yi), 1 < i < n, is an i.i.d. sample of (p + 1)-variate vectors, where each Yi is at risk of being censored from the right and Xi is a vector of observable covariables. Under weak moment assumptions we show that a linear functional of the correspond- ing (p + 1)-dimensional Kaplan-Meier estimator is asymptotically normal. Applications to censored correlation, regression and mean residual lifetime estimation are discussed in greater detail.