ADAPTIVE TRIMMED MEAN AS A LOCATION ESTIMATE

作     者：Siming LI Yong LI Jiao JIN Siming LI;Yong LI;Jiao JIN

作者机构：Department of Statistics and Financial MathematicsSchool of Mathematical SciencesKey Laboratory of Mathematics and Complex SystemsMinistry of EducationBeijing Normal University 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2012年第25卷第5期

页      面：973-986页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0814[工学-土木工程] 0701[理学-数学] 082301[工学-道路与铁道工程] 0823[工学-交通运输工程] 

基　　金：supported in part by the National Basic Research Program of China under Grant No. 2010CB950703 the Natural Science Foundation of China under Grant No.10901020 the Fundamental Research Funds for the Central Universities 

主　　题：Adaptive location parameter robust trimmed mean. 

摘      要：The trimmed mean is one of the most common estimators of location for symmetrical distributions, whose effect depends on whether the trim rate matches the proportion of contaminated data. Based on the geometric characteristics of the curve of the trimmed variance function, the authors propose two kinds of adaptive trimmed mean algorithms. The accuracy of the estimators is compared with that of other often-used estimates, such as sample mean, trimmed mean, trimean, and median, by means of simulation method. The results show that the accuracy of the adaptive derivative optimization trimmed mean method is close to the optimum performance in case of medium contamination （the contamination rate is less than 50%）. Under high contamination situation （the contamination rate equals 8070）, the performance of the estimates is comparable to that of the median and is superior to other counterparts.