1. 論説：高次元小標本における統計的仮説検定 石井 晶, 矢田和善, 青嶋 誠 数学 73 (2021)
2. 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 (2020)
3. Hypothesis tests for high-dimensional covariance structures ISHII Aki, YATA Kazuyoshi, AOSHIMA Makoto Annals of the Institute of Statistical Mathematics (2020)
4. 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)
5. 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 - 1286 (2020)
6. 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 - 142 (2019)
7. 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)
8. 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 - 128 (2019)
9. 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 - 503 (2019)
10. High-dimensional quadratic classifiers in non-sparse settings AOSHIMA Makoto, YATA Kazuyoshi Methodology and Computing in Applied Probability 21 663 - 682 (2019)

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