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矢田和善

Yata, Kazuyoshi

数理物質系 , 教授
Institute of Pure and Applied Sciences , Professor

オープンアクセス版の論文は「つくばリポジトリ」で読むことができます。

1. Automatic Sparse PCA for High-Dimensional Data Yata, Kazuyoshi; Aoshima, Makoto
Statistica Sinica 35: 1069 (2025) Semantic Scholar
2. Proposing a Low-Rank Approximation Method with Mathematical Guarantees for High-Dimensional Tensor Data HASEGAWA, Hiroki; YATA, Kazuyoshi; OKADA, Yukihiko; KUNIMATSU, Jun
Prceedings of 2024 IEEE International Conference on Big Data (BigData) 34 (2024)
3. Noise Reduced Common PCA for High-Dimensional, Low-Sample Size Multi-View Data Hasegawa, Hiroki; Kawamura, Homura; Shin, Ryota; Yata, Kazuyoshi (+1 著者) Kunimatsu, Jun
Proceedings of the 6th International Conference on Statistics: Theory and Applications (2024)
4. 高次元統計解析で探る銀河の分子ガスの物理状態と天文学への展望青嶋, 誠; 矢田和善
統計数理 72: 273 (2024)
5. High Dimensional Statistical Analysis and its Application to an ALMA Map of NGC 253 Takeuchi; Tsutomu T.; Yata, Kazuyoshi; Egashira, Kento (+5 著者) Kono, Kai T.
The Astrophysical Journal Supplement Series 271: 44 (2024) Semantic Scholar
6. Asymptotic Properties of Hierarchical Clustering in High-Dimensional Settings Egashira, Kento; Yata, Kazuyoshi; Aoshima, Makoto
JOURNAL OF MULTIVARIATE ANALYSIS 199: 105251 (2024) Semantic Scholar
7. Test for High-Dimensional Outliers with Principal Component Analysis Nakayama, Yugo; Yata, Kazuyoshi; Aoshima, Makoto
Japanese Journal of Statistics and Data Science Epub: (2024) Semantic Scholar
8. 階層的クラスタリングの高次元漸近的性質について江頭健斗; 矢田, 和善; 青嶋誠
数理解析研究所講究録 2221: 30 (2022)
9. Consistency of the objective general index in high-dimensional settings Takuma, Bando; Tomonari, Sei; Yata, Kazuyoshi
Journal of Multivariate Analysis 189: 104938 (2022) Semantic Scholar
10. Geometric classifiers for high-dimensional noisy data (Editor's invited paper) Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
Special Issue: 50th Anniversary Jubilee Edition, Journal of Multivariate Analysis 188: 104850 (2022)
11. Geometric classifiers for high-dimensional noisy data (Editor's invited paper) Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
Special Issue: 50th Anniversary Jubilee Edition, Journal of Multivariate Analysis 188: 104850 (2022) Semantic Scholar
12. Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings Nakayama, Yugo; Yata, Kazuyoshi; Aoshima, Makoto
JOURNAL OF MULTIVARIATE ANALYSIS 185: 104779 (2021) Semantic Scholar
13. Asymptotic properties of distance-weighted discrimination and its bias correction for high-dimension, low-sample-size data Egashira, Kento; Yata, Kazuyoshi; Aoshima, Makoto
JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 4: 821 (2021) Semantic Scholar
14. Hypothesis tests for high-dimensional covariance structures Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
Annals of the Institute of Statistical Mathematics 73: 599 (2021) Semantic Scholar
15. Hypothesis tests for high-dimensional covariance structures Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
Annals of the Institute of Statistical Mathematics 73: 599 - 622 (2021) Semantic Scholar
16. 論説：高次元小標本における統計的仮説検定青嶋, 誠; 石井, 晶; 矢田和善
数学 73: 360 (2021)
17. 高次元におけるDistanceWeighted Discriminationについて江頭健斗; 矢田和善; 青嶋誠
数理解析研究所講究録 1 (2020)
18. High-dimensional covariance matrix estimation under the SSE model Konishi, Keisuke; Yata, Kazuyoshi; Aoshima, Makoto
数理解析研究所講究録 11 (2020)
19. Tests for high-dimensional covariance structures under the non-strongly spiked eigenvalue model Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
数理解析研究所講究録 21 (2020)
20. 単一強スパイク固有値モデルにおける高次元平均ベクトルの2標本検定石井晶; 矢田和善; 青嶋誠
応用統計学 49: 109 (2020)

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1. 高次元の統計学青嶋, 誠; 矢田, 和善
共立出版 2019年4月 (ISBN: 9784320112636)

21. A high-dimensional quadratic classifier after feature selection Yata, Kazuyoshi; Aoshima, Makoto
International Symposium on Statistical Theory and Methodology for Large Complex Data 2018年11月26日招待有り
22. Equality tests of high-dimensional covariance matrices on the basis of strongly spiked eigenvalues Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
Waseda International Symposium “Introduction of General Causality to Various Data & Its Innovation of The Optimal Inference” 2018年10月24日招待有り
23. Equality tests for high-dimensional covariance matrices Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
The 27th South Taiwan Statistics Conference 2018年6月30日招待有り
24. Regularized PCA for high-dimensional data based on the noise reduction methodology Yata, Kazuyoshi; Aoshima, Makoto
Fifth Institute of Mathematical Statistics Asia Pacific Rim Meeting 2018年6月26日招待有り
25. Inference on high-dimensional mean vectors under the strongly spiked eigenvalue model Yata, Kazuyoshi; Aoshima, Makoto; Ishii, Aki
The 9th International Workshop on Applied Probability 2018年6月18日招待有り
26. Consistency properties of regularized noise reduction methodology in high-dimensional settings Yata, Kazuyoshi; Aoshima, Makoto
The 4th International Society of NonParametric Statistics Conference 2018年6月13日招待有り
27. Regularized noise-reduction methodology for high-dimensional data Yata, Kazuyoshi; Aoshima, Makoto
10th Conference of the IASC-ARS/68th Annual NZSA Conference 2017年12月13日招待有り
28. High-dimensional correlation tests with sample size determination Yata, Kazuyoshi; Aoshima, Makoto
Sixth International Workshop in Sequential Methodologies 2017年6月22日招待有り
29. Estimation of low-rank matrices in high-dimensional settings Yata,Kazuyoshi
A Symposium on Complex Data Analysis 2017 2017年5月26日招待有り
30. Inference on high-dimensional covariance structures via the extended cross-data-matrix methodology Yata, Kazuyoshi; Aoshima, Makoto
Eighth International Workshop on Applied Probability 2016年6月23日招待有り
31. Effective classifiers for high-dimensional non-sparse data Yata, Kazuyoshi; Aoshima, Makoto
International conference on information complexity and statistical modeling in high dimensions with applications 2016年5月20日招待有り
32. Power spiked モデルをもつ高次元データの固有値推定について矢田,和善; 青嶋誠
日本数学会年度会 2013年3月21日
33. PCA Consistency for Power Spiked Model in High-Dimensional Settings 矢田,和善; 青嶋誠
日本統計学会春季集会 2013年3月3日招待有り
34. PCA consistency for high-dimensional data under generalized models 矢田,和善; 青嶋誠
日本数学会秋季総合分科会 2012年9月20日
35. Effective PCA for large p, small n scenario under generalized models Yata,Kazuyoshi
Sixth International Workshop on Applied Probability 2012年6月14日招待有り
36. Asymptotic properties of k-means and its bias correction under high dimensional settings Kento, Egashira; Kazuyoshi, Yata; Makoto, Aoshima
18th International Joint Conference on Computational and Financial Econometrics and Computational and Methodological Statistics 招待有り
37. Statistical inference on high-dimensional covariance structures under the SSE models Ishii, Aki; Iwana, Yumu; YATA, Kazuyoshi; Aoshima, Makoto
International Symposium on Theories, Methodologies and Applications for Large Complex Data 招待有り
38. Asymptotic properties of k-means and its modification under high dimensional settings Egashira, Kento; YATA, Kazuyoshi; Aoshima, Makoto
The 2nd Joint Conference on Statistics and Data Science in China 招待有り
39. Asymptotic properties of high-dimensional PCA and its application YATA, Kazuyoshi; Aoshima, Makoto
The 8th International Workshop in Sequential Methodologies 招待有り
40. Asymptotic properties of hierarchical clustering for high-dimensional data Egashira, Kento; YATA, Kazuyoshi; Aoshima, Makoto
The 8th International Workshop in Sequential Methodologies 招待有り