为解决传统简单随机设计(抽样)在很多情形下效率过低问题，统计学家提出了一些新的设计，如拉丁超立方体设计、正交和均匀设计等。排序集抽样(RSS)设计由于其特有的特点和高效率性被广泛应用于农业、环境、生态和医学等领域。RSS下的研究主要集中在某个无偏估计类中的最小方差无偏估计(MVUE)或单参数模型的优良RSS设计和这种RSS下的单参数的极大似然估计(MLE)。于是，如何针对一般多参数分布族提出一种优良RSS，并在这种优良RSS下获得这类分布族中多参数的一致MVUEs或MLEs，成为了RSS理论研究中的一个热点和难点问题。最重要和最常见的多参数分布族是多参数截尾分布族。本项目将根据此分布族的特征，提出一种优良RSS并在此优良RSS下研究该模型中多参数的MLEs问题。这项研究即可丰富传统的抽样设计理论，也可完善RSS下的统计推断理论。因此，本项目无论在理论上还是在应用前景上都具有重要的研究价值。

The traditional simple random design(sampling) method is inefficient in many cases. Statisticians proposed some new designs to increase efficiency such as Latin hypercube design、orthogonal and uniform design etc.. Ranked set sampling (RSS) is widely applied in agriculture, environment, ecology and medicine areas etc. as its characteristic and high efficiency. Research on RSS were mainly focused on the minimum variance unbiased estimator(MVUE) in a class of some unbiased estimators or maximum likelihood estimator(MLE) of the parameter for one-parameter underlying distribution under the optimal RSS. Hence, how to proposed an optimal RSS for multi-parameters family and obtain the MVUEs or MLEs of the parameters for this family under the optimal RSS will be a hot and difficult problem in RSS theorem. The multi-parameters truncated family is very important and usual. In this project, we will propose an optimal RSS according to the character of the family and study MLEs of the parameters for the family under the optimal RSS. This project can be both enriched traditional sample design theorem and supplemented RSS statistical inference theory. Hence, in view of application and theoretical development, our project is very meaningful and valuable.