报告题目：Optimal estimation of location and scale parameters under moving extreme ranked set sampling

报告人：陈望学 吉首大学教授、数学与统计学院副院长

时间：2025年4月11日14：00-17：00

会议地点：图书馆一楼报告厅

个人简介：陈望学，吉首大学教授，数学与统计学院副院长，系“芙蓉计划”-湖湘青年英才。主要研究方向是数据采样理论与方法和计算机实验的设计与分析，以第一完成人获湖南省自然科学奖三等奖1项，主持国家自然科学项目2项，在国内外重要期刊发表科研论文48篇(其中SCI收录28篇)。

摘要：In this talk, the best linear unbiased estimators (BLUEs) of location and scale parameters from 1ocation-scale family will be respectively proposed in cases when one parameter is known and when both are unknown under moving extremes ranked set sampling (MERSS). Explicit mathematical expressions of these estimators and their variances are derived. Their efficiencies with respect to the minimum variance unbiased estimators (MVUES) under simple random sampling (SRS) are compared for the cases of some usual distributions. The numerical results show that the BLUEs under MERSS are significantly more efficient than the MVUEs under SRS. Furthermore, the best linear invariant estimators (BLIEs) of location and scale parameters from location-scale family will be respectively proposed in cases when one parameter is known and when both are unknown under MERSS. It is theoretically proved that the BLIE and the BLUE of location parameter are identical for scale parameter known and they have the same efficiency, the BLIE of scale parameter is more efficient than the BLUE of scale parameter for the location parameter known and the BLIEs of location and scale parameters are more efficient than corresponding the BLUEs of location and scale parameters for both unknown. The efficiencies of BLIEs with respect to the minimum variance invariant estimators (MVIEs) under SRS are compared for the cases of some usual distributions. The numerical results show that the BLIEs are significantly more efficient than the MVIEs under SRS.