Estimation of P[Y < X] for generalized exponential distribution
Estimation of P[Y < X] for generalized exponential distribution
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DOI：
10.1007/s001840400345
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发表时间：
2005-06-01
期刊：
影响因子：
0.7
通讯作者：
Gupta, RD
中科院分区：
文献类型：
--
作者：
Kundu, D;Gupta, RD
本文讨论了当X和Y是两个独立的广义指数分布，具有不同的形状参数，但具有相同的尺度参数时，P [Y < X]的估计。得到了极大似然估计及其渐近分布。渐近分布用于构造P [Y < X]的渐近置信区间。在公共尺度参数已知的条件下，得到了P [Y < X]的极大似然估计、一致最小方差无偏估计和Bayes估计。提出了不同的置信区间。蒙特卡洛模拟进行比较不同的方法。为了说明的目的，还介绍了模拟数据集的分析。
This paper deals with the estimation of P [Y < X] when X and Y are two independent generalized exponential distributions with different shape parameters but having the same scale parameters. The maximum likelihood estimator and its asymptotic distribution is obtained. The asymptotic distribution is used to construct an asymptotic confidence interval of P [Y < X]. Assuming that the common scale parameter is known, the maximum likelihood estimator, uniformly minimum variance unbiased estimator and Bayes estimator of P [Y < X] are obtained. Different confidence intervals are proposed. Monte Carlo simulations are performed to compare the different proposed methods. Analysis of a simulated data set has also been presented for illustrative purposes.