Multivariate analysis of variance with fewer observations than the dimension

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On the sphericity test with large-dimensional observations (2013, background): “by large-dimensional data. Consequently, modiﬁcations and corrections are needed. The reader can ﬁnd in Johnstone and Titterington (2009) for a review and in Ledoit and Wolf (2002), Srivastava(2005), Srivastava and Fujikoshi (2006), Schott(2007), Bai et al. (2009), Chen et al. (2010), Chen and Qin (”
On testing the equality of high dimensional mean vectors with unequal covariance matrices (2014, background): “Since then, more high dimensional tests have been proposed by Bai and Saranadasa (1996), Fujikoshi et al. (2004), Srivastava and Fujikoshi (2006), Srivastava (2007), Schott (2007), Srivastava and Du (2008), Srivastava (2009), Srivastava and Yanagihara (2010), Chen and Qin (2010) and Srivastava et…”
Multivariate Theory for Analyzing High Dimensional Data (2007, background): “For some other tests and power, see Srivastava and Fujikoshi (2006) , and when (p/n) → c, c ∈ (0, ∞), see Fujikoshi et al. (2004).”