Gemayel Nader, Stasny Elizabeth A, Wolfe Douglas A
JPMorgan Chase, Columbus, OH, USA.
Lifetime Data Anal. 2015 Apr;21(2):315-29. doi: 10.1007/s10985-014-9312-x. Epub 2014 Oct 19.
Ranked set sampling (RSS) is a data collection technique that combines measurement with judgment ranking for statistical inference. This paper lays out a formal and natural Bayesian framework for RSS that is analogous to its frequentist justification, and that does not require the assumption of perfect ranking or use of any imperfect ranking models. Prior beliefs about the judgment order statistic distributions and their interdependence are embodied by a nonparametric prior distribution. Posterior inference is carried out by means of Markov chain Monte Carlo techniques, and yields estimators of the judgment order statistic distributions (and of functionals of those distributions).
秩集抽样（RSS）是一种数据收集技术，它将测量与用于统计推断的判断排序相结合。本文为RSS构建了一个形式化且自然的贝叶斯框架，该框架类似于其频率主义的合理性证明，并且不需要完美排序的假设或使用任何不完美排序模型。关于判断顺序统计量分布及其相互依赖性的先验信念由非参数先验分布体现。后验推断通过马尔可夫链蒙特卡罗技术进行，并产生判断顺序统计量分布（以及这些分布的泛函）的估计量。
