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MULTIPLE COMPARISON PROCEDURES FOR HIGH-DIMENSIONAL DATA AND THEIR ROBUSTNESS UNDER NON-NORMALITY
2013
Journal of the Japanese Society of Computational Statistics
This paper analyzes whether procedures for multiple comparison derived in Hyodo et al. (2013) work for an unbalanced case and under non-normality. We focus on pairwise multiple comparisons and comparisons with a control among mean vectors, and show that the asymptotic properties of these procedures remain valid in an unbalanced high-dimensional setting. We also numerically justify that the derived procedures are robust under non-normality, i.e., the coverage probability of these procedures can
doi:10.5183/jjscs.1211001_202
fatcat:nohp3ewmajarpfggj2xwnre2gi