Authors' Response
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Publication:3106538
DOI10.1080/07474946.2011.619102zbMath1284.62499OpenAlexW4214717409MaRDI QIDQ3106538
Makoto Aoshima, Kazuyoshi Yata
Publication date: 28 December 2011
Published in: Sequential Analysis (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/2241/117753
classificationrobustnessconfidence regionvariable selectionsample size determinationtwo-sample testHDLSScross-data-matrix methodology
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Related Items (7)
Geometric Classifier for Multiclass, High-Dimensional Data ⋮ Statistical inference for high-dimension, low-sample-size data ⋮ Correlation tests for high-dimensional data using extended cross-data-matrix methodology ⋮ PCA consistency for the power spiked model in high-dimensional settings ⋮ A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise ⋮ Asymptotic normality for inference on multisample, high-dimensional mean vectors under mild conditions ⋮ A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data
Cites Work
- Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations
- Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix
- Inference on high-dimensional mean vectors with fewer observations than the dimension
- A two-sample test for high-dimensional data with applications to gene-set testing
- 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
- Sequential Procedures for Selecting the Most Probable Multinomial Cell When a Nuisance Cell Is Present
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