Discussion on “Two-Stage Procedures for High-Dimensional Data” by Makoto Aoshima and Kazuyoshi Yata
DOI10.1080/07474946.2011.619094zbMath1284.62500OpenAlexW1988086583MaRDI QIDQ5894438
Pinyuen Chen, Subrahmanian Panchapakesan
Publication date: 28 December 2011
Published in: Sequential Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/07474946.2011.619094
Multivariate distribution of statistics (62H10) Parametric tolerance and confidence regions (62F25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Hypothesis testing in multivariate analysis (62H15) Central limit and other weak theorems (60F05) Sequential statistical analysis (62L10)
Cites Work
- Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations
- Asymptotic second-order consistency for two-stage estimation methodologies and its applications
- Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix
- Effective Two-Stage Estimation for a Linear Function of High-Dimensional Gaussian Means
- Intrinsic Dimensionality Estimation of High-Dimension, Low Sample Size Data withD-Asymptotics
- PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context
- Geometric Representation of High Dimension, Low Sample Size Data
- The high-dimension, low-sample-size geometric representation holds under mild conditions
- Estimating the Total Probability of the Unobserved Outcomes of an Experiment
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