Ranked set sampling (RSS) utilizes auxiliary information on the variable of interest so as to assist the experimenter in acquiring an informative sample from the population. The resulting sample has a stratified structure, and often improves statistical inference with respect to the simple random sample of comparable size. In RSS literature, there are some goodness-of-fit tests based on the empirical estimators of the in-stratum cumulative distribution functions (CDFs). Motivated by the fact that the in-stratum CDFs in RSS can be expressed as functions of the population CDF, some new tests are developed and their asymptotic properties are explored. An extensive simulation study is performed to evaluate properties of different testing procedures when the parent distribution is normal. It turns out that the proposed tests can be considerably more powerful than their contenders in many situations. An application in the context of fishery is also provided.

排序集抽样 (RSS) 利用感兴趣变量的辅助信息，以帮助实验者从总体中获取信息样本。得到的样本具有分层结构，并且通常可以改进与大小可比的简单随机样本相关的统计推断。在 RSS 文献中，有一些基于层内累积分布函数 (CDF) 的经验估计量的拟合优度检验。由于 RSS 中的层内 CDF 可以表示为总体 CDF 的函数，因此开发了一些新的测试并探索了它们的渐近特性。当母体分布正常时，进行了广泛的模拟研究以评估不同测试程序的特性。事实证明，在许多情况下，所提议的测试可能比它们的竞争者强大得多。还提供了渔业背景下的应用。