| 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 | 21 | 2015 |
| 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 |
| 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 | 13 | 2017 |
| 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 |
| On the conservative simultaneous confidence procedures for multiple comparisons among mean vectors T Seo, T Nishiyama Journal of statistical planning and inference 138 (11), 3448-3456, 2008 | 8 | 2008 |
| On test statistics in profile analysis with high-dimensional data M Onozawa, T Nishiyama, T Seo Communications in Statistics-Simulation and Computation 45 (10), 3716-3743, 2016 | 6 | 2016 |
| The multivariate Tukey-Kramer multiple comparison procedure among four correlated mean vectors T Nishiyama, T Seo American Journal of Mathematical and Management Sciences 28 (1-2), 115-130, 2008 | 6 | 2008 |
| 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 |
| 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 | 5 | 2017 |
| 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 |
| A one-sample location test based on weighted averaging of two test statistics when the dimension and the sample size are large M Hyodo, T Nishiyama Communications in Statistics-Theory and Methods 46 (7), 3526-3541, 2017 | 3 | 2017 |
| Recent developments of multivariate multiple comparisons among mean vectors T Nishiyama, M Hyodo, T Seo SUT journal of Mathematics 50 (2), 247-270, 2014 | 3 | 2014 |
| On the simultaneous confidence procedure for multiple comparisons with a control T Nishiyama SUT Journal of Mathematics 43 (2), 137-147, 2007 | 3 | 2007 |
| Testing for independence of high-dimensional variables: ρV-coefficient based approach M Hyodo, T Nishiyama, T Pavlenko Journal of Multivariate Analysis 178, 104627, 2020 | 2 | 2020 |
| A one-sample location test based on weighted averaging of two test statistics in high-dimensional data M Hyodo, T Nishiyama arXiv preprint arXiv:1405.2370, 2014 | 1 | 2014 |
| Approximation to the upper percentiles of the statistic for pairwise comparison among components of mean vector in elliptical distributions S Takahashia, T Nishiyamab, T Seob Technical Report 10–06, Hiroshima Statistical Research Group, Hiroshima …, 2010 | 1 | 2010 |
| On the conservative multivariate Tukey-Kramer type procedures for multiple comparisons among mean vectors T Seoa, T Nishiyamab submitted for publication, 2006 | 1 | 2006 |
| The Multivariate Tukey-Kramer Multiple Comparison Procedure Among Four Correlated Mean Vectors T Seoa, T Nishiyamab Technical Report, 2006 | 1 | 2006 |
| A Behrens–Fisher problem for general factor models in high dimensions M Hyodo, T Nishiyama, T Pavlenko Journal of Multivariate Analysis 195, 105162, 2023 | | 2023 |
