MULTIPLE COMPARISON PROCEDURES FOR HIGH-DIMENSIONAL DATA AND THEIR ROBUSTNESS UNDER NON-NORMALITY

Sho Takahashi, Masashi Hyodo, Takahiro Nishiyama, Tatjana Pavlenko
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
more » ... e controlled with or without the assumption of normality of the data.
doi:10.5183/jjscs.1211001_202 fatcat:nohp3ewmajarpfggj2xwnre2gi