| Support vector machine and its bias correction in high-dimension, low-sample-size settings Y Nakayama, K Yata, M Aoshima Journal of statistical planning and inference 191, 88-100, 2017 | 28 | 2017 |
| Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings Y Nakayama, K Yata, M Aoshima Journal of Multivariate Analysis 185, 104779, 2021 | 24 | 2021 |
| Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings Y Nakayama, K Yata, M Aoshima Annals of the Institute of Statistical Mathematics 72, 1257-1286, 2020 | 19 | 2020 |
| Robust support vector machine for high-dimensional imbalanced data Y Nakayama Communications in Statistics-Simulation and Computation 50 (5), 1524-1540, 2021 | 10 | 2021 |
| A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise K Yata, M Aoshima, Y Nakayama Sequential Analysis 37 (3), 397-411, 2018 | 7 | 2018 |
| Support vector machine and optimal parameter selection for high-dimensional imbalanced data Y Nakayama Communications in Statistics-Simulation and Computation 51 (11), 6739-6754, 2022 | 2 | 2022 |
| Asymptotic properties of support vector machines in HDLSS settings (Bayes Inference and Its Related Topics) Y Nakayama, K Yata, M Aoshima 数理解析研究所講究録 2047, 10-18, 2017 | 2 | 2017 |
| Test for high-dimensional outliers with principal component analysis Y Nakayama, K Yata, M Aoshima Japanese Journal of Statistics and Data Science, 1-28, 2024 | | 2024 |
| International Symposium on New Developments of Theories and Methodologies for Large Complex Data M Aoshima, M Sato-Ilic, K Yata, A Ishii, Y Nakayama | | 2021 |
| Support vector machine in high-dimension, low-sample-size settings Y Nakayama | | 2020 |
| Soft-margin SVMs in the HDLSS context (最尤法とベイズ法) Y Nakayama, K Yata, M Aoshima 数理解析研究所講究録, 44-55, 2019 | | 2019 |
| Soft-margin SVMs in the HDLSS context (Maximum Likelihood and Bayesian Methods) Y Nakayama, K Yata, M Aoshima 数理解析研究所講究録 2124, 44-55, 2019 | | 2019 |
| A general framework of SVM in HDLSS settings (Statistical Inference and Modelling) Y Nakayama, K Yata, M Aoshima 数理解析研究所講究録 2091, 14-21, 2018 | | 2018 |
| 高次元小標本におけるサポートベクターマシンの一致性について (Statistical Inference on Divergence Measures and Its Related Topics) 中山優吾， 矢田和善， 青嶋誠 数理解析研究所講究録 1999, 17-27, 2016 | | 2016 |
| Soft-margin SVMs in the HDLSS context (Maximum Y Nakayama, K Yata, M Aoshima Sequential Analysis 34, 279-294, 0 | | |
| A general framework of SVM in HDLSS settings (Statistical Y Nakayama, K Yata, M Aoshima | | |
| Soft‐margin SVMs in the HDLSS context Y Nakayama, K Yata | | |
