Takuma, Bando, Tomonari, Sei, Yata, Kazuyoshi. Consistency of the objective general index in high-dimensional settings. Journal of Multivariate Analysis. 2022. 189. 104938-104938
Ishii, Aki, Yata, Kazuyoshi, Aoshima, Makoto. Geometric classifiers for high-dimensional noisy data (Editor's invited paper). Special Issue: 50th Anniversary Jubilee Edition, Journal of Multivariate Analysis. 2022. 188. 104850
Nakayama, Yugo, Yata, Kazuyoshi, Aoshima, Makoto. Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings. JOURNAL OF MULTIVARIATE ANALYSIS. 2021. 185. 104779-104779
Egashira, Kento, Yata, Kazuyoshi, Aoshima, Makoto. Asymptotic properties of distance-weighted discrimination and its bias correction for high-dimension, low-sample-size data. JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE. 2021. 4. 821-840
Estimation of eigenvectors for linear combinations of high-dimensional covariance matrices and its application
(The 5th International Conference on Econometrics and Statistics)
Test for outlier detection by high-dimensional PCA
(The 5th International Conference on Econometrics and Statistics)
Asymptotic behaviors of hierarchical clustering under high dimensional settings
(The 5th International Conference on Econometrics and Statistics)
強スパイク固有値モデルにおける高次元統計的推測
(応用統計学会年会特別講演)
Asymptotic properties of high-dimensional kernel PCA and its applications
(International Symposium on New Developments of Theories and Methodologies for Large Complex Data 2021)