Testing linear hypotheses of mean vectors for high-dimension data with unequal covariance matrices T Nishiyama, M Hyodo, T Seo, T Pavlenko Journal of Statistical Planning and Inference 143 (11), 1898-1911, 2013 | 39 | 2013 |
Testing block‐diagonal covariance structure for high‐dimensional data M Hyodo, N Shutoh, T Nishiyama, T Pavlenko Statistica Neerlandica 69 (4), 460-482, 2015 | 22 | 2015 |
An asymptotic approximation for EPMC in linear discriminant analysis based on two-step monotone missing samples N Shutoh, M Hyodo, T Seo Journal of multivariate analysis 102 (2), 252-263, 2011 | 19 | 2011 |
Multiple comparisons among mean vectors when the dimension is larger than the total sample size M Hyodo, S Takahashi, T Nishiyama Communications in Statistics-Simulation and Computation 43 (10), 2283-2306, 2014 | 18 | 2014 |
Asymptotic expansion and estimation of EPMC for linear classification rules in high dimension T Kubokawa, M Hyodo, MS Srivastava Journal of Multivariate Analysis 115, 496-515, 2013 | 18 | 2013 |
Testing block-diagonal covariance structure for high-dimensional data under non-normality Y Yamada, M Hyodo, T Nishiyama Journal of Multivariate Analysis 155, 305-316, 2017 | 14 | 2017 |
Asymptotic properties of the misclassification rates for Euclidean distance discriminant rule in high-dimensional data H Watanabe, M Hyodo, T Seo, T Pavlenko Journal of Multivariate Analysis 140, 234-244, 2015 | 13 | 2015 |
A variable selection criterion for linear discriminant rule and its optimality in high dimensional and large sample data M Hyodo, T Kubokawa Journal of multivariate Analysis 123, 364-379, 2014 | 13 | 2014 |
Two-way MANOVA with unequal cell sizes and unequal cell covariance matrices in high-dimensional settings H Watanabe, M Hyodo, S Nakagawa Journal of Multivariate Analysis 179, 104625, 2020 | 10 | 2020 |
A simultaneous testing of the mean vector and the covariance matrix among two populations for high-dimensional data M Hyodo, T Nishiyama Test 27 (3), 680-699, 2018 | 10 | 2018 |
Modified Jarque-Bera type tests for multivariate normality in a high-dimensional framework K Koizumi, M Hyodo, T Pavlenko Journal of Statistical Theory and Practice 8, 382-399, 2014 | 10 | 2014 |
Kick-one-out-based variable selection method for Euclidean distance-based classifier in high-dimensional settings T Nakagawa, H Watanabe, M Hyodo Journal of Multivariate Analysis 184, 104756, 2021 | 9 | 2021 |
A modified linear discriminant analysis for high-dimensional data M Hyodo, T Yamada, T Himeno, T Seo Hiroshima Mathematical Journal 42 (2), 209-231, 2012 | 8 | 2012 |
Estimation of misclassification probability for a distance-based classifier in high-dimensional data H Watanabe, M Hyodo, Y Yamada, T Seo Hiroshima Mathematical Journal 49 (2), 175-193, 2019 | 7 | 2019 |
Evaluation of multinomial logistic regression models for predicting causative pathogens of food poisoning cases H Inoue, T Suzuki, M Hyodo, M Miyake Journal of Veterinary Medical Science 80 (8), 1223-1227, 2018 | 6 | 2018 |
Bartlett correction to the likelihood ratio test for MCAR with two‐step monotone sample N Shutoh, T Nishiyama, M Hyodo Statistica Neerlandica 71 (3), 184-199, 2017 | 6 | 2017 |
Asymptotic power comparison of T2-type test and likelihood ratio test for a mean vector based on two-step monotone missing data M Hyodo, N Shutoh Communications in Statistics-Theory and Methods 49 (17), 4270-4287, 2020 | 5 | 2020 |
On error bounds for high-dimensional asymptotic distribution of L2-type test statistic for equality of means M Hyodo, T Nishiyama, T Pavlenko Statistics & Probability Letters 157, 108637, 2020 | 5 | 2020 |
On simultaneous confidence interval estimation for the difference of paired mean vectors in high-dimensional settings M Hyodo, H Watanabe, T Seo Journal of Multivariate Analysis 168, 160-173, 2018 | 5 | 2018 |
Multiple comparison procedures for high-dimensional data and their robustness under non-normality S Takahashi, M Hyodo, T Nishiyama, T Pavlenko Journal of the Japanese Society of Computational Statistics 26 (1), 71-82, 2013 | 5 | 2013 |