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Journal of Probability and Statistics
Volume 2009, Article ID 373572, 9 pages
Research Article

Data Depth Trimming Counterpart of the Classical (or ) Procedure

Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA

Received 20 July 2009; Revised 11 October 2009; Accepted 14 October 2009

Academic Editor: Zhidong Bai

Copyright © 2009 Yijun Zuo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The classical (or in high dimensions) inference procedure for unknown mean is so fundamental in statistics and so prevailing in practices; it is regarded as an optimal procedure in the mind of many practitioners. It this manuscript we present a new procedure based on data depth trimming and bootstrapping that can outperform the classical (or in high dimensions) confidence interval (or region) procedure.