Bayes Estimation for the Marshall–Olkin Exponential Distribution
研究了在串联和并联系统随机样本下，Marshall–Olkin指数分布参数的贝叶斯估计，给出了精确和近似的最高后验密度可信椭球，并比较了两种抽样下估计的渐近精度。
SUMMARY Bayes estimators of the parameters of the Marshall–Olkin exponential distribution are obtained when random samples from series and parallel systems are available. The estimators are with respect to the quadratic loss function, and the prior distribution allows for prior dependence among the components of the parameter vector. Exact and approximate highest posterior density credible ellipsoids for the parameters are also obtained. In contrast with series sampling, the Bayes estimators under parallel sampling are not in closed form, and numerical procedures are required to obtain estimates. Bayes estimators of the reliability functions are also given. The gain in asymptotic precision of parallel estimates over series estimates is also ascertained theoretically.