[1]. Yamada, T. (2007). On comparisons of exact powers of bivariate GMANOVA tests. Communications in Statistics—Theory and Methods, 36, 399–413.
[2]. Sakaori, F., Yamada, T., Kawamura, A., and Sugiyama, T. (2007). A new confidence interval for all characteristic roots of a covariance matrix. Computational Statistics, 22, 121-131.
[3]. Fujikoshi, Y., Yamada, T., Watanabe, D. and Sugiyama, T. (2007). Asymptotic distribution of the LR statistic for equality of the smallest eigenvalues in high-dimensional principal component analysis. Journal of Multivariate Analysis, 98, 2002-2008.
[4]. Kato, N., Yamada, T. and Fujikoshi, Y. (2009). High-dimensional asymptotic expansion of LR statistic for testing intraclass correlation structure and its error bound. Journal of Multivariate Analysis, 101, 101-112.
[5]. Hyodo, M. and Yamada, T. (2009). Asymptotic distribution of the studentized cumulative contribution ratio in high-dimensional principal components analysis. Communications in Statistics—Simulation and Computation, 38, 905-917.
[6]. Hyodo, M. and Yamada, T. (2010). Asymptotic properties of the EPMC for modified linear discriminant analysis when sample size and dimension are both large. Journal of Statistical Planning and Inference, 140, 2739-2748.
[7]. Yamada, T. and Sakurai, T. (2012). Asymptotic power comparison of three tests in GMANVA when the number of observed points is large. Statistics and Probability Letters. 82, 692-698.
[8]. Yamada, T. (2012). High-dimensional Edgeworth expansion of LR statistic for testing circular symmetric covariance structure and its error bound. Communications in Statistics—Theory and Methods. 41, 1887-1910.
[9]. Yamada, T. and Srivastava, M.S. (2012). A test for multivariate analysis of variance in high - dimension. Communications in Statistics—Theory and Methods. 41, 2602-2615.
[10]. Yamada, T. (2012). Note on asymptotic null distributions of LR statistics for testing covariance matrix under growth curve model when the number of the observation points is large. SUT Journal of Mathematics, 48, 37-46.
[11]. Hyodo, M., Yamada T. and Srivastava, M.S. (2012). A model selection criterion for discriminant analysis of high-dimensional data with fewer observations. Journal of Statistical Planning and Inference, 142, 3134-3145.
[12].Hyodo, M., Yamada, T., Himeno, T. and Seo, T. (2012). A modified linear discriminant analysis for high-dimensional data. Hiroshima Math Journal, 42, 209-231.
[13] Yamada, T., Hyodo, M. and Seo, T. (2013). The asymptotic approximation of EPMC for linear discriminant rules using a Moore-Penrose inverse matrix in high dimension, Communications in Statistics—Theory and Methods. 42, 3329-3338.
[14] Himeno, T. and Yamada, T. (2014). Estimations for some functions of covariance matrix in high dimension under non-normality and its applications, Journal of multivariate analysis, 130, 27-34.
[15] Yamada, T. and Himeno, T. (2015). Testing homogeneity of mean vectors under heteroscedasticity in high-dimension, Journal of multivariate analysis, 139, 7-27.
[16] Yamada, T., Himeno, T. and Sakurai, T. (2017). Interval estimation in discriminant anslysis for large dimension, Communication in Statistics-Theory and Methods, 46, 9042-9052.
[17] Yamada, T., Himeno, T. and Sakurai, T. (2017). Asymptotic cut-off point in linear discriminant rule to adjust the misclassification probability for large dimensions, Hiroshima Mathematical Journal, 4, 319-348.
[18] Yamada, T. (2018). Interval estimation in two-group discriminant analysis under heteroscedasticity for large dimension, Accepted in Communication in Statistics-Theory and Methods.
[19] Yamada, T. and Himeno, T. (2019). Estimation of multivariate 3rd moment for high-dimensional data and its application for testing multivariate normality, Computational Statistics, 34, 911–941.
[20] Yamada, T. (2019). Constrained linear discriminant rule for 2-groups via the Studentized classification statistic W for large dimension, SUT Journal of Mathematics, 55, 69-93.
[21] Yamada, T., Sakurai, T. and Fujikoshi,Y. (2020). High-dimensional asymptotic results for EPMCs of W- and Z- rules, Communications in Statistics-Theory and Methods, 51, 2385-2413.
[22] Yamada, T. (2022). High-dimensional asymptotic expansion of the null distribution for Schott’s test statistic for complete independence of normal random variables, Communications in Statistics-Theory and Methods, accepted.
[23] Yamada, T. and Himeno, T. (2023). High-dimensional asymptotic expansion of the null distribution for L2 norm based MANOVA testing statistic under general distribution, Journal of Statistical Planning and Inference, 224, 9-26.