2022
Nakayama, Y. (2022).
Support vector machine and optimal parameter selection for high-dimensional imbalanced data.
Communications in Statistics – Simulation and Computation 51(11), 6739–6754. (doi: 10.1080/03610918.2020.1813300)
2021
[OPEN ACCESS]
Nakayama, Y., Yata, K. and Aoshima, M. (2021).
Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings.
Journal of Multivariate Analysis 185 104779. (doi:10.1016/j.jmva.2021.104779)
Nakayama, Y. (2021).
Robust support vector machine for high-dimensional imbalanced data
Communications in Statistics – Simulation and Computation 50(5) 1524-1540.(doi: 10.1080/03610918.2019.15)
2020
Nakayama. Y., Yata, K. and Aoshima, M. (2020).
Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings.
Annals of the Institute of Statistical Mathematics 72(5), 1257- 1286. (doi: 10.1007/s10463-019-00727-1)
2019
Nakayama., Y. (2019).
Robust support vector machine for high-dimensional imbalanced data.
Communications in Statistics – Simulation and Computation 50 (5), 1524-1540. (doi: 10.1080/03610918.2020.1813300)
2018
Yata, K., Aoshima, M. and Nakayama. Y. (2018).
A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise.
Sequential Analysis 37(3), 397-411. (doi: 10.1080/03610918.2019.1586922)
2017
Nakayama. Y., Yata, K. and Aoshima, M. (2017).
Support vector machine and its bias correction in high-dimension, low-sample-size settings.
Journal of Statistical Planning and Inference 191, 88-100. (doi:10.1016/j.jspi.2017.05.005)