Multivariate Statistics: High-Dimensional and Large-Sample Approximations

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Independence test for high dimensional data based on regularized canonical correlation coefficients (2015, background): “Details can be referred to Chapter 11 of [11].”
On the Optimality of Averaging in Distributed Statistical Learning (2014, background): “ xed symmetric p pmatrix. It follows that: (i) E[S 1] = ; (ii) E[S 1BS 1] = 2 B + Tr( B) , and (iii) E[S 1BS 2BS 1] = 2 B B + Tr( B B) . 22 Proof. The rst two statements are simply an application of [Fujikoshi et al., 2010, Theorem 2.2.5]. To prove (iii) we write S1 ij = (S 1) ijand recall the indep”
High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification (2015, background): “In another line of work, a Japanese school (e.g., Fujikoshi et al., 2011, and references therein) has studied the error rates of LDA and RDA under high-dimensional asymptotics, with a focus on obtaining accurate higher-order expansions to their risk.”