ホーム > 青嶋 誠/ Aoshima, Makoto

青嶋 誠

Aoshima, Makoto

数理物質系 数学域 , 教授
Institute of Mathematics , Professor

https://www.math.tsukuba.ac.jp/~aoshima-lab/jp/

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 estimation of the high-dimensional cross-covariance matrix Tetsuya Umino; Kazuyoshi Yata; Makoto Aoshima
      Journal of Multivariate Analysis 213: 105590 (2026) Semantic Scholar
  2. 2. Correlation tests for high-dimensional data under the strongly spiked eigenvalue model Yumu Iwana; Aki Ishii; Kazuyoshi Yata; Makoto Aoshima
      Japanese Journal of Statistics and Data Science (2026) Semantic Scholar
  3. 3. Asymptotic Properties of Kernel K-means and Its Comparison with Conventional K-means in High-Dimensional Settings Kento Egashira; Kazuyoshi Yata; Makoto Aoshima
      Procedia Computer Science 270: 495 (2025) Semantic Scholar
  4. 4. Automatic Sparse PCA for High-Dimensional Data Kazuyoshi Yata; Makoto Aoshima
      Statistica Sinica 35: 1069 (2025) Semantic Scholar
  5. 5. Test for high-dimensional outliers with principal component analysis Yugo Nakayama; Kazuyoshi Yata; Makoto Aoshima
      Japanese Journal of Statistics and Data Science (2024) Semantic Scholar
  6. 6. High-dimensional Statistical Analysis and Its Application to an ALMA Map of NGC 253 Tsutomu T. Takeuchi; Kazuyoshi Yata; Kento Egashira; Makoto Aoshima (+4 著者) Kai T. Kono
      The Astrophysical Journal Supplement Series 271: 44 (2024) Semantic Scholar
  7. 7. Asymptotic properties of hierarchical clustering in high-dimensional settings Kento Egashira; Kazuyoshi Yata; Makoto Aoshima
      Journal of Multivariate Analysis 199: 105251 (2024) Semantic Scholar
  8. 8. Geometric classifiers for high-dimensional noisy data ISHII Aki; YATA Kazuyoshi; AOSHIMA Makoto
      Special Issue: 50th Anniversary Jubilee Edition, Journal of Multivariate Analysis 188: 104850 (2022) Semantic Scholar
  9. 9. 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
  10. 10. 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)
  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. 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. Geometric consistency of principal component scores for high-dimensional mixture models and its application YATA Kazuyoshi, AOSHIMA Makoto
      Scandinavian Journal of Statistics 47: 899 (2020) Semantic Scholar
  14. 14. 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)
  15. 15. 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)
  16. 16. Inference on high-dimensional mean vectors under the strongly spiked eigenvalue model ISHII Aki; YATA Kazuyoshi; AOSHIMA Makoto
      Japanese Journal of Statistics and Data Science 2: 105 (2019)
  17. 17. 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)
  18. 18. High-dimensional quadratic classifiers in non-sparse settings AOSHIMA Makoto; YATA Kazuyoshi
      Methodology and Computing in Applied Probability 21: 663 (2019)
  19. 19. A quadratic classifier for high-dimension, low-sample-size data under the strongly spiked eigenvalue model ISHII Aki, YATA Kazuyoshi, AOSHIMA Makoto
      Stochastic Models, Statistics and Their Applications, Proceedings of the 14th Workshop on Stochastic Models, Statistics and their Application 131 (2019)
  20. 20. 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 (2019)
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  1. 1. 高次元の統計学 青嶋 誠; 矢田和善 (担当:共著)
    共立出版 2019年
  2. 2. Effective Methodologies for Statistical Inference on Microarray Studies AOSHIMA Makoto; YATA Kazuyoshi (担当:分担執筆)
    InTech 2011年
  3. 3. 知識ベース知識の森 青嶋 誠 (担当:分担執筆, 範囲:12群 電子情報通信基礎: 3編 統計・確率)
    電子情報通信学会 2011年
  4. 4. Recent Advances in Statistical Inference- in Honor of Professor Masafumi Akahira AOSHIMA Makoto
    Taylor & Francis 2010年
  5. 5. Asymptotic Second-Order Efficiency for Two-Stage Multiple Comparisons with Components of a Linear Function of Mean Vectors AOSHIMA Makoto; KUSHIDA Takuya (担当:分担執筆)
    Birkhauser 2005年
  6. 6. Two-Stage Procedures for Selecting the Best Component of a Multivariate Exponential Distribution AOSHIMA Makoto; AOKI Mitsuru; KAI Masaki (担当:分担執筆)
    Dekker 2004年
  7. 7. Bounded Risk Point Estimation of a Linear Function of K Multinormal Mean Vectors When Covariance Matrices Are Unknown AOSHIMA Makoto; TAKADA Yoshikazu (担当:分担執筆)
    Taylor & Francis 2002年
  8. 8. Multivariate Statistical Analysis in Honor of Professor Minoru Siotani, Vol. 3 HAYAKAWA Takesi; AOSHIMA Makoto; SHIMIZU Kunio
    American Sciences Press 1997年
  9. 9. Multivariate Statistical Analysis in Honor of Professor Minoru Siotani, Vol. 2 HAYAKAWA Takesi; AOSHIMA Makoto; SHIMIZU Kunio
    American Sciences Press 1996年
  10. 10. 多変量解析
    数理情報科学事典 朝倉書店 1995年
  11. 11. Book review of "Multistage Selection and Ranking Procedures : Second-Order Asymptotics, Mukhopadhyay and Solanky, Marcel Dekker, Inc.(1994)"
    J. Japan Statist. Soc. 1995年
  12. 12. Multivariate Statistical Analysis in Honor of Professor Minoru Siotani, Vol. 1 HAYAKAWA Takesi; AOSHIMA Makoto; SHIMIZU Kunio
    American Sciences Press 1995年
  13. 13. Note on Testing the Goodness-of-Fit for Intraclass Correlation Model SIOTANI Minoru; AOSHIMA Makoto (担当:分担執筆)
    Elsevier Science Publishers 1988年
  1. 1. 高次元小標本の統計学－次元の呪いを超えて (基調講演) 青嶋 誠
    CREST １細胞数理 第3回会合 2026年3月16日 招待有り
  2. 2. 総合討論 青嶋 誠
    日本学術会議公開シンポジウム「AI時代における統計科学・データサイエンスの役割と挑戦－公平性、信頼性、解釈可能性、AIガバナンスの観点から」 2026年2月17日 招待有り
  3. 3. AIの不確実性への挑戦－高次元小標本の統計学からのアプローチ (招待講演) 青嶋 誠
    日本学術会議公開シンポジウム 「AI時代における統計科学・データサイエンスの役割と挑戦－公平性、信頼性、解釈可能性、AIガバナンスの観点から」 2026年2月17日 招待有り
  4. 4. 高次元小標本の統計学 (特別講演) 青嶋 誠
    中央大学理工学研究所2025年度第1回特別講演会 2025年10月28日 招待有り
  5. 5. High-dimension, low-sample-size analysis: automatic sparse estimation and geometric insights in non-sparse spiked models (基調講演) AOSHIMA Makoto
    The 8th International Conference on Econometrics and Statistics (EcoSta 2025), Waseda University 2025年8月 招待有り
  6. 6. Non-Sparse Modeling for High-Dimensional Data (特別招待講演) AOSHIMA Makoto
    Statistische Woche 2023, Dortmund, Germany 2023年9月 招待有り
  7. 7. 高次元現象の統計数理（企画特別講演） 青嶋 誠
    日本数学会2022年度秋季総合分科会 2022年9月 招待有り
  8. 8. 高次元小標本の統計学：非スパース性と巨大ノイズ（特別講演） 青嶋 誠
    統計数理研究所リスク解析戦略研究センターシンポジウム 2021年9月 招待有り
  9. 9. High-Dimensional Statistical Analysis: Non-Sparsity, Strongly Spiked Noise and HDLSS (基調講演) AOSHIMA Makoto
    The 7th International Workshop in Sequential Methodologies, State University of New York, U.S.A. 2019年6月 招待有り
  10. 10. Non-Sparse Modeling for High-Dimensional Data (招待講演) AOSHIMA Makoto
    Waseda International Symposium “Introduction of General Causality to Various Data & its Applications”, Waseda University 2019年2月 招待有り
  11. 11. New Techniques in High-Dimensional Statistical Analysis: SSE vs. NSSE and Data Transformation (基調講演) AOSHIMA Makoto
    2018 Workshop on High-Dimensional Statistical Analysis, Academia Sinica, Taiwan 2018年12月 招待有り
  12. 12. High-Dimensional Statistical Analysis: Non-Sparse Modeling, Geometric Representations and New PCAs (基調講演) AOSHIMA Makoto
    2018 Workshop on High-Dimensional Statistical Analysis, Academia Sinica, Taiwan 2018年12月 招待有り
  13. 13. New techniques in high-dimensional statistical analysis (招待講演) AOSHIMA Makoto
    Waseda International Symposium “Introduction of General Causality to Various Data & Its Innovation of The Optimal Inference”, Waseda University 2018年10月 招待有り
  14. 14. 計量生物学における高次元統計解析の可能性（招待講演） 青嶋 誠
    2018年度統計関連学会連合大会，中央大学 2018年9月 招待有り
  15. 15. High-dimensional statistical analysis: Spiked models and data transformation（特別招待講演） AOSHIMA Makoto
    The 2nd International Conference on Econometrics and Statistics, City University of Hong Kong, Hong Kong 2018年6月 招待有り
  16. 16. High-dimensional statistical analysis under spiked models（招待講演） AOSHIMA Makoto
    The Fourth Conference of the International Society for Nonparametric Statistics, Salerno, Italy 2018年6月 招待有り
  17. 17. High-Dimensional Statistical Analysis by Non-Sparse Modeling（招待講演） AOSHIMA Makoto
    Waseda International Symposium “Recent Developments in Time Series Analysis: Quantile Regression, High Dimensional Data & Causality", Waseda University 2018年2月 招待有り
  18. 18. 高次元統計解析：理論・方法論とその周辺（日本統計学会賞受賞者記念講演） 青嶋 誠
    2017年度統計関連学会連合大会，南山大学 2017年9月 招待有り
  19. 19. PCA based clustering for ultrahigh-dimensional data（招待講演） AOSHIMA Makoto
    The 1st International Conference on Econometrics and Statistics, Hong Kong University of Science and Technology, Hong Kong 2017年6月 招待有り
  20. 20. High-dimensional Statistical Analysis for the SSE Model（招待講演） AOSHIMA Makoto
    A Symposium on Complex Data Analysis 2017, National Tsing Hua University, Hsinchu, Taiwan 2017年5月 招待有り
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