Minimum Distance Estimators of Scale With Censored Data
研究了删失程度如何影响基于Kaplan-Meier估计与假设模型之间加权Cramér-von Mises距离最小化的尺度参数估计量，证明了渐近正态性，并比较了指数分布下最小距离估计量与最大似然估计量的置信区间覆盖概率。
This article examines how the level of censoring affects the performance of estimators of scale that minimize a weighted Cramérvon Mises distance between the Kaplan-Meier product-limit estimator of the survival distribution function and an assumed model. The article shows that under certain conditions these estimators are asymptotically normal if the survival function is a member of the assumed scale family. Weights that minimize the asymptotic variance under these conditions also are found. Under stronger conditions, the minimum distance estimators (MDE's) are asymptotically normal even if the survival function is not a member of the assumed scale family. Asymptotic coverage probabilities of confidence intervals are found for percentiles constructed from exponential MDE's of scale under departures from the exponential distribution, and what happens in the limiting cases of no censoring and complete censoring is examined. Using the asymptotic results and Monte Carlo experiments, several exponential MDE's of scale are compared to the exponential maximum likelihood estimator (MLE) of scale. The confidence intervals constructed from some of the MDE's have coverage probabilities closer to that expected under exponentiality than does the MLE at most censoring levels.