青嶋 誠(アオシマ マコト)
- 会議発表等
- Regularized PCA for high-dimensional data based on the noise reduction methodology
Yata Kazuyoshi; Aoshima Makoto
Fifth Institute of Mathematical Statistics Asia Pacific Rim Meeting/2018-06-26--2018-06-26 - Regularized noise-reduction methodology for high-dimensional data
Yata Kazuyoshi; Aoshima Makoto
10th Conference of the IASC-ARS/68th Annual NZSA Conference/2017-12-13 - High-dimensional correlation tests with sample size determination
Yata Kazuyoshi; Aoshima Makoto
Sixth International Workshop in Sequential Methodologies/2017-06-22 - High-Dimensional Statistical Analysis by Non-Sparse Modeling
Aoshima Makoto
Waseda International Symposium “Recent Developments in Time Series Analysis: Quantile Regression, High Dimensional Data & Causality/2018-02-27 - 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/2017-09-05 - Effective classifiers for high-dimensional non-sparse data
Yata Kazuyoshi; Aoshima Makoto
International conference on information complexity and statistical modeling in high dimensions with applications/2016-05-20 - Inference on high-dimensional covariance structures via the extended cross-data-matrix methodology
Yata Kazuyoshi; Aoshima Makoto
Eighth International Workshop on Applied Probability/2016-06-23 - PCA based clustering for ultrahigh-dimensional data
Aoshima Makoto; Yata Kazuyoshi
The 1st International Conference on Econometrics and Statistics/2017-06-15 - High-dimensional Statistical Analysis for the SSE Model
Aoshima Makoto
A Symposium on Complex Data Analysis 2017/2017-05-26 - High-dimensional statistical analysis based on the inference of eigenstructures
Aoshima Makoto
Waseda International Symposium “High Dimensional Statistical Analysis for Time Spatial Processes & Quantile Analysis for Time Series”/2017-02-28 - Statistical inference in strongly spiked eigenvalue models
Aoshima Makoto
International Symposium “Statistical Analysis for Large Complex Data”/2016-11-23 - Reconstruction of a high-dimensional low-rank matrix and its applications
Aoshima Makoto
Waseda International Symposium “High Dimensional Statistical Analysis for Time Spatial Processes, Quantile and Empirical Likelihood Analysis for Time Series”/2016-10-25 - Inference on High-Dimensional Covariance Structures via the Extended Cross-Data-Matrix Methodology
Aoshima Makoto
The Eighth International Workshop on Applied Probability/2016-06-23 - Effective Classifiers for High-Dimensional Non-Sparse Data
Aoshima Makoto
International Conference on Information Complexity and Statistical Modeling in High Dimensions with Applications/2016-05-20 - 高次元固有空間の推測と高次元統計解析
Aoshima Makoto
第11回日本統計学会春季集会/2017-03-05 - 統計学の話題と研究の可能性
Aoshima Makoto
筑波大学CiRfSE ワークショップ/2017-01-24 - スパイクノイズと高次元統計解析
Aoshima Makoto
日本学術振興会科学研究費による研究集会「数理統計ひこね2016」/2016-12-02 - High-dimensional two-sample tests under strongly spiked eigenvalue models
Aoshima Makoto
研究集会「大規模統計モデリングと計算統計III」/2016-09-27 - Reconstruction of a high-dimensional low-rank matrix
Yata Kazuyoshi; Aoshima Makoto
2016年度統計関連学会連合大会/2016-09-07 - High-Dimensional Quadratic Classifiers in Non-Sparse Settings under Heteroscedasticity
Aoshima Makoto
ISNPS Meeting “Biosciences, Medicine, and novel Non-Parametric Methods”/2015-07-15 - 高次元の統計学(再び)
Aoshima Makoto
「統計数理および金融数理研究」セミナー/2016-04-25 - Inference on high-dimensional covariance structures with fewer observations than the dimension
Yata Kazuyoshi; Aoshima Makoto
Waseda International Symposium “High Dimensional Statistical Analysis for Time Spatial Processes & Quantile Analysis for Time Series”/2016-03-01 - Two-sample tests of high-dimensional means under the strongly spiked eigenvalue model
Yata Kazuyoshi; Aoshima Makoto
Waseda International Symposium "High Dimensional Statistical Analysis for Spatio-Temporal Processes & Quantile Analysis for Time Series"/2015-11-10 - PCA consistency for high-dimensional multiclass mixture models and its applications
Yata Kazuyoshi; Aoshima Makoto
ISNPS Meeting “Biosciences, Medicine, and novel Non-Parametric Methods”/2015-07-13 - Two-Sample Tests of High-Dimensional Means Under the Strongly Spiked Eigenvalue Model
Aoshima Makoto
Waseda International Symposium “High Dimensional Statistical Analysis for Time Spatial Processes and Quantile Analysis for Time Series”/2015-11-10 - さらに表示...
- Regularized PCA for high-dimensional data based on the noise reduction methodology