Abstract
The classicalt(orT2in high dimensions) inference procedure for unknown meanμ: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 classicalt(orT2in high dimensions) confidence interval (or region) procedure.
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