ホーム > 矢田和善/ Yata, Kazuyoshi

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。

21. Geometric consistency of principal component scores for high-dimensional mixture models and its application, Yata, Kazuyoshi; Aoshima, Makoto
Scandinavian Journal of Statistics 47: 899 - 921 (2020) Semantic Scholar
22. Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings Nakayama, Yugo; Yata, Kazuyoshi; Aoshima, Makoto
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 72: 1257 (2020) Semantic Scholar
23. A quadratic classifier for high-dimension, low-sample-size data under the strongly spiked eigenvalue model Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
Proceedings of the 14th Workshop on Stochastic Models, Statistics and their Application 131 (2019) Semantic Scholar
24. High-Dimensional Quadratic Classifiers in Non-sparse Settings Aoshima, Makoto; Yata, Kazuyoshi
METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY 21: 663 (2019) Semantic Scholar
25. Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models Aoshima, Makoto; Yata, Kazuyoshi
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 71: 473 (2019) Semantic Scholar
26. Inference on high-dimensional mean vectors under the strongly spiked eigenvalue model Isii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
Japanese Journal of Statistics and Data Science 2: 105 - 128 (2019) Semantic Scholar
27. Equality tests of high-dimensional covariance matrices under the strongly spiked eigenvalue model Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
Journal of Statistical Planning and Inference 202: 99 - 111 (2019) Semantic Scholar
28. A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise Kazuyoshi Yata; Makoto Aoshima; Yugo Nakayama
SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS 37: 397 (2018) Semantic Scholar
29. Two-sample tests for high-dimension, strongly spiked eigenvalue models Makoto Aoshima; Kazuyoshi Yata
Statistica Sinica 28: 43 (2018) Semantic Scholar
30. Statistical inference for high-dimension, low-sample-size data Aoshima, Makoto; Yata, Kazuyoshi
American Mathematical Society, Sugaku Expositions 30: 137 (2017)
31. 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
RIMS Kokyuroku 1999: 17 (2016)
32. Estimation of a signal matrix for high-dimensional non-Gaussian data (Statistical Inference on Divergence Measures and Its Related Topics) 矢田, 和善; 青嶋, 誠
RIMS Kokyuroku 1999: 36 (2016)
33. Geometric Classifier for Multiclass, High-Dimensional Data Makoto Aoshima; Kazuyoshi Yata
SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS 34: 279 (2015) Semantic Scholar
34. Reconstruction of a signal matrix for high-dimension, low-sample-size data (New Advances in Statistical Inference and Its Related Topics) 村山, 航; 矢田, 和善; 青嶋, 誠
RIMS Kokyuroku 1954: 23 (2015)
35. 拡張クロスデータ行列法と共分散行列関数の不偏推定矢田, 和善; 青嶋, 誠
RIMS Kokyuroku 1954: 51 (2015)
36. 高次元小標本における混合データの幾何学的表現とクラスター分析への応用 (Asymptotic Statistics and Its Related Topics) 矢田, 和善; 青嶋, 誠
RIMS Kokyuroku 1910: 125 (2014)
37. 高次元データの統計的方法論(日本統計学会研究業績賞受賞者特別寄稿論文) 青嶋, 誠; 矢田, 和善
日本統計学会誌. シリーズJ 43: 123 (2013)
38. Inference on High-Dimensional Mean Vectors with Fewer Observations Than the Dimension Kazuyoshi Yata; Makoto Aoshima
METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY 14: 459 (2012) Semantic Scholar
39. Asymptotic properties of a distance-based classifier for high-dimensional data (A New Perspective to Statistical Models and Its Related Topics) 矢田, 和善; 青嶋, 誠
RIMS Kokyuroku 1804: 53 (2012)
40. Note on classification for high-dimensional data (A New Perspective to Statistical Models and Its Related Topics) 永橋, 幸大; 矢田, 和善; 青嶋, 誠
RIMS Kokyuroku 1804: 40 (2012)

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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)

1. Asymptotic properties of high-dimensional kernel PCA and its applications Nakayama, Yugo; Yata, Kazuyoshi; Aoshima, Makoto
International Symposium on New Developments of Theories and Methodologies for Large Complex Data 2021年11月6日招待有り
2. Sparse PCA for high-dimensional data based on the noise-reduction methodology and its application Yata, Kazuyoshi; Aoshima, Makoto
The 63rd ISI World Statistics Congress 2021年7月14日招待有り
3. Clustering by kernel PCA with Gaussian kernel and tuning for high-dimensional data Nakayama, Yugo; Yata, Kazuyoshi; Aoshima, Makoto
The 4th International Conference on Econometrics and Statistics 2021年6月26日招待有り
4. Tests for covariance structures in high-dimensional data Yata, Kazuyoshi; Ishii, Aki; Aoshima, Makoto
The 4th International Conference on Econometrics and Statistics 2021年6月26日招待有り
5. High-dimensional classifiers under the strongly spiked eigenvalue model Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
The 4th International Conference on Econometrics and Statistics 2021年6月25日招待有り
6. High-dimensional quadratic classifiers under the strongly spiked eigenvalue model Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
IISA 2021 Conference 2021年5月21日招待有り
7. 高次元におけるカーネル主成分分析の漸近的性質と異常値の検出への応用中山優吾; 矢田和善; 青嶋誠
日本数学会年度年会 2021年3月16日
8. 距離加重判別分析の高次元漸近的性質江頭健斗; 矢田和善; 青嶋誠
日本数学会年度年会 2021年3月16日
9. 高次元固有ベクトルの検定について石井晶; 矢田, 和善; 青嶋誠
日本数学会秋季総合分科会 2020年9月24日
10. Clustering by kernel principal component analysis for high-dimensional data 中山優吾; 矢田和善; 青嶋誠
日本数学会秋季総合分科会 2020年9月24日
11. 高次元データにおける距離加重判別分析の漸近的性質とバイアス補正江頭健斗; 矢田和善; 青嶋誠
統計関連学会連合大会 2020年9月11日
12. 高次元小標本における異常値の検出中山優吾; 矢田和善; 青嶋誠
統計関連学会連合大会 2020年9月10日
13. Tests of high-dimensional correlation matrices under the strongly spiked eigenvalue model 石井晶; 矢田和善; 青嶋誠
統計関連学会連合大会 2020年9月10日
14. 高次元スパースPCAの一致性とその応用矢田和善; 青嶋誠
統計関連学会連合大会 2020年9月10日
15. Tests for high-dimensiomal covariance structures under the SSE model Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
International Symposium on Theories and Methodologies for Large Complex Data 2019年11月21日招待有り
16. Inference on mean vectors for high-dimensional data with the strongly spiked eigenstructure Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
The 3rd International Conference on Econometrics and Statistics 2019年6月26日招待有り
17. A high-dimensional quadratic classifier by data transformation for strongly spiked eigenvalue models Yata, Kazuyoshi; Ishii, Aki; Aoshima, Makoto
The 3rd International Conference on Econometrics and Statistics 2019年6月26日招待有り
18. A high-dimensional quadratic classifier under the strongly spiked eigenvalue model Yata, Kazuyoshi; Ishii, Aki; Aoshima, Makoto
The 14th Workshop on Stochastic Models, Statistics and their Application 2019年3月6日招待有り
19. Tests of High-Dimensional Mean Vectors and Its Application Under the SSE Model Ishii, Aki; Yata, Kazuyoshi; Aoshima, Makoto
Waseda International Symposium “Introduction of General Causality to Various Data & its Applications” 2019年2月26日招待有り
20. Tests of high-dimensional mean vectors under the SSE model Ishii, Aki; 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