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

逆問題の手法で「非破壊検査」
逆問題研究リサーチユニット
Non-Destructive Inspection by the Technique of Inverse Problem

2017-03-21

青嶋 誠 矢田 和善
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. 1. Automatic Sparse PCA for High-Dimensional Data Yata, Kazuyoshi; Aoshima, Makoto
      Statistica Sinica 35: 1069 (2025) Semantic Scholar
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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)

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