title: Low bias mean estimator for symmetric and asymmetric unimodal distributions.
abstract: An adaptive trimmed mean estimator for symmetric and asymmetric distributions has been developed. The simulation sample size as small as n = 10, 25 and 50 are chosen, where the influence of the outliers increases in small sample data. The comparison of the proposed estimator and the Princeton Robust Study are made. The results show that the proposed mean estimator has lower standard deviation for symmetric distributions and has a better location estimation for asymmetric distributions.
authors.affiliation: Department of Electrical Engineering, Far East College, 49 Jung-Haw Road, Tainan 74404, Taiwan. juneyule@ms10.hinet.net
authors.forename: J-Y
authors.initials: JY
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D016011:Normal Distribution; D015203:Reproducibility of Results; D015983:Selection Bias