青嶋 誠(アオシマ マコト)
- 論文
- A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise
Yata Kazuyoshi; Aoshima Makoto; Nakayama Yugo
SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS/37(3)/pp.397-411, 2018 - Two-sample tests for high-dimension, strongly spiked eigenvalue models
Aoshima Makoto; Yata Kazuyoshi
Statistica Sinica/28(1)/pp.43-62, 2018 - Statistical inference for high-dimension, low-sample-size data
Aoshima Makoto; Yata Kazuyoshi
American Mathematical Society, Sugaku Expositions/30/pp.137-158, 2017 - A survey of high dimension low sample size asymptotics
Aoshima Makoto; Shen Dan; Shen Haipeng; Yata Kazuyosh...
AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS/60(1:::SI)/pp.4-19, 2018-03 - Support vector machine and its bias correction in high-dimension, low-sample-size settings
Nakayama Yugo; Yata Kazuyoshi; Aoshima Makoto
JOURNAL OF STATISTICAL PLANNING AND INFERENCE/191/pp.88-100, 2017-12 - 高次元の統計学
Aoshima Makoto
数学通信/21/pp.5-15, 2016-08 - Non-asymptotic results for Cornish-Fisher expansions
Ulyanov V.V.; Aoshima Makoto; Fujikoshi Y.
Journal of Mathematical Sciences/218/pp.363-368, 2016-04 - Reconstruction of a high-dimensional low-rank matrix
Yata Kazuyoshi; Aoshima Makoto
Electronic Journal of Statistics/10(1)/pp.895-917, 2016 - High-dimensional inference on covariance structures via the extended cross-data-matrix methodology
Yata Kazuyoshi; Aoshima Makoto
JOURNAL OF MULTIVARIATE ANALYSIS/151/pp.151-166, 2016-10 - 高次元小標本におけるサポートベクターマシンの一致性について (Statistical Inference on Divergence Measures and Its Related Topics)
中山 優吾; 矢田 和善; 青嶋 誠
数理解析研究所講究録/1999/pp.17-27, 2016-07 - Estimation of a signal matrix for high-dimensional non-Gaussian data (Statistical Inference on Divergence Measures and Its Related Topics)
矢田 和善; 青嶋 誠
数理解析研究所講究録/1999/pp.36-46, 2016-07 - Reconstruction of a High-Dimensional Low-Rank Matrix
Yata Kazuyoshi; Aoshima Makoto
Electronic Journal of Statistics/10/pp.895-917, 2016-03 - Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context
Ishii Aki; Yata Kazuyoshi; Aoshima Makoto
JOURNAL OF STATISTICAL PLANNING AND INFERENCE/170/pp.186-199, 2016-03 - Geometric Classifier for Multiclass, High-Dimensional Data
Aoshima Makoto; Yata Kazuyoshi
SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS/34(3:::SI)/pp.279-294, 2015-07 - Reconstruction of a signal matrix for high-dimension, low-sample-size data (New Advances in Statistical Inference and Its Related Topics)
村山 航; 矢田 和善; 青嶋 誠
数理解析研究所講究録/1954/pp.23-31, 2015-06 - 拡張クロスデータ行列法と共分散行列関数の不偏推定
矢田 和善; 青嶋 誠
数理解析研究所講究録/1954/pp.51-60, 2015-06 - Largest Eigenvalue Estimation for High-Dimension, Low-Sample-Size Data and its Application (Asymptotic Statistics and Its Related Topics : RIMS共同研究報告集)
石井 晶; 矢田 和善; 青嶋 誠
数理解析研究所講究録/1910/pp.115-124, 2014-08 - 高次元小標本における混合データの幾何学的表現とクラスター分析への応用 (Asymptotic Statistics and Its Related Topics)
矢田 和善; 青嶋 誠
数理解析研究所講究録/1910/pp.125-133, 2014-08 - On the distribution of the largest eigenvalue in high dimension, low sample size context
矢田 和善; 青嶋 誠
数理解析研究所講究録/1860/pp.120-128, 2013-11 - Correlation tests for high-dimensional data using extended cross-data-matrix methodology
Yata Kazuyoshi; Aoshima Makoto
JOURNAL OF MULTIVARIATE ANALYSIS/117/pp.313-331, 2013-05 - A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data
Aoshima Makoto; Yata Kazuyoshi
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS/66(5)/pp.983-1010, 2014-10 - Asymptotic normality for inference on multisample, high-dimensional mean vectors under mild conditions
Aoshima Makoto; Yata Kazuyoshi
Methodology and Computing in Applied Probability/17(2)/pp.419-439, 2015-06 - 高次元データの統計的方法論(日本統計学会研究業績賞受賞者特別寄稿論文)
青嶋 誠; 矢田 和善
日本統計学会誌. シリーズJ/43(1)/pp.123-150, 2013-09 - PCA consistency for the power spiked model in high-dimensional settings
Yata Kazuyoshi; Aoshima Makoto
JOURNAL OF MULTIVARIATE ANALYSIS/122/pp.334-354, 2013-11 - 論説： 高次元小標本における統計的推測
青嶋 誠; 矢田和善
數學/65(3)/pp.225-247, 2013-07 - さらに表示...
- A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise