Papers

[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. (doi:10.1016/j.jmva.2021.104779)

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)

Nakayama. Y. (2020).

Support vector machine and optimal parameter selection for high-dimensional imbalanced data.

Communications in Statistics – Simulation and Computation (doi: 10.1080/03610918.2020.1813300)

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)

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)

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)