矢田 和善 / Yata, Kazuyoshi

数理物質系 准教授 / Associate Professor, Faculty of Pure and Applied Sciences

関連記事 / Related Articles in COTRE

2023年度第6回「数理物質系学際セミナー」矢田 和善・石井 宏幸
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

 

論文 / Publication

  1. Hypothesis tests for high-dimensional covariance structures Ishii, Aki, Yata, Kazuyoshi, Aoshima, Makoto ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS Epub (2020)
  2. Hypothesis tests for high-dimensional covariance structures Ishii, Aki, Yata, Kazuyoshi, Aoshima, Makoto ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS Epub (2020)
  3. Geometric consistency of principal component scores for high-dimensional mixture models and its application Yata, Kazuyoshi, Aoshima, Makoto SCANDINAVIAN JOURNAL OF STATISTICS Epub (2019)
  4. 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 - 142 (2019)
  5. 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)
  6. 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 (3) 473 - 503 (2019)
  7. 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 (1) 105 - 128 (2019)
  8. Two-sample tests for high-dimension, strongly spiked eigenvalue models Makoto Aoshima, Kazuyoshi Yata 28 (1) 43 - 62 (2018)
  9. A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise Kazuyoshi Yata, Makoto Aoshima, Yugo Nakayama 37 (3) 397 - 411 (2018)
  10. Statistical inference for high-dimension, low-sample-size data Aoshima, Makoto, Yata, Kazuyoshi American Mathematical Society, Sugaku Expositions 30 137 - 158 (2017)

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発行:筑波大学 URA研究戦略推進室・研究推進部