学术报告
报告题目：An Algorithm and A Strategy for Nonparametric Inference under Imperfect Rankings in Ranked-Set Sampling
报告人：张一旻 助理教授，美国维拉诺瓦大学 数学与统计系
报告时间：2017年5月31日 下午14:00—15:00
报告地点：研教楼418
报告校内联系人：王晓光 84708351-8013
报告摘要：Ranked set sampling has been successfully applied in fields such as forestry, entomology, medicine and environmental monitoring. In this talk, I will introduce a simple general strategy for approximate nonparametric inference in ranked-set sampling. This strategy involves of fitting a one-parameter model for imperfect rankings and then using an inference procedure that offers guaranteed good performance under the fitted model. The imperfect rankings model is fitted through a nonparametric maximum likelihood scheme that is made possible by a new algorithm for computing the probability that specified independent mixtures of order statistics have a particular ordering. I will illustrate the general strategy by using it to obtain confidence bands for the distribution function and confidence intervals for population quantiles. It will be shown through simulation that in both cases, the new strategy performs as well or better than existing methods and it performs well even when the data-generating model differs from the one-parameter model that is fitted.

报告人简介：Dr. Yimin Zhang is an assistant professor in Department of Mathematics and Statistics at Villanova University, US. She received her B.S. in Computational Mathematics from Dalian University of Technology, China, and Ph.D. in Statistics from Oklahoma State University. Dr. Zhang’s statistical methodological research interests include multiple comparisons, inference with ranked-set sampling, and statistical learning. Her collaborative work spans agriculture, transportation, ecology, and education.
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大连理工大学数学科学学院
统计与金融研究所
2017年5月24日