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