Data Depth Trimming Counterpart of the Classical t (or T2)‎ Procedure

Abstract EN

The classical t (or T2 in high dimensions) inference procedure for unknown mean μ:X¯±tα(n−1)Sn/n (or {μ:n(x¯−μ)′S−1(x¯−μ)≤χ(1−α)2(p)}) 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 t (or T2 in high dimensions) confidence interval (or region) procedure.