随机删失下经验贝叶斯估计的渐近最优性

Asymptotical Optimality of Empirical Bayes Estimation Under Random Censorship

该文运用经验贝叶斯(empirical Bayes(简称EB))方法,在历史样本和当前样本均被另一个具有未知分布的变量随机右删失的条件下,构造了一个指数分布参数的经验贝叶斯估计并获得了它的渐近最优性．文章最后给出了一个例子和模拟结果．

In this paper, using empirical Bayes （EB） approach, the author constructs an EB estimator for the parameter of the exponential distribution under the condition that the present sample and the past samples are randomly censored from the right by another variable with an unknown distribution and obtains its asymptotical optimality, An example is illustrated to show that Theorem＇s conditions are easily satisfied. Also, the author simulates the Theorem＇s results.