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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. 1. Automatic Sparse PCA for High-Dimensional Data Yata, Kazuyoshi; Aoshima, Makoto
      Statistica Sinica (2025) Semantic Scholar
  2. 2. 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)
  3. 3. 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
  4. 4. Asymptotic Properties of Hierarchical Clustering in High-Dimensional Settings Egashira, Kento; Yata, Kazuyoshi; Aoshima, Makoto
      Journal of Multivariate Analysis 199: 105251 (2024) Semantic Scholar
  5. 5. 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
  6. 6. 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)
  7. 7. 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
  8. 8. 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)
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. 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)
  15. 15. 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)
  16. 16. 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)
  17. 17. 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)
  18. 18. 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)
  19. 19. 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
  20. 20. 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
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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)
  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
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