Test for high-dimensional outliers with principal component analysis

Cites Work

Unnamed Item
PCA consistency for the power spiked model in high-dimensional settings
Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations
Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context
Sparse principal component analysis via regularized low rank matrix approximation
Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix
Outlier identification in high dimensions
Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models
Subspace rotations for high-dimensional outlier detection
Hypothesis tests for high-dimensional covariance structures
Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings
High-dimensional outlier detection using random projections
An adjusted Grubbs' and generalized extreme studentized deviation
Equality tests of high-dimensional covariance matrices under the strongly spiked eigenvalue model
Percentage Points for a Generalized ESD Many-Outlier Procedure
Outlier detection for high-dimensional data
Two-sample tests for high-dimension, strongly spiked eigenvalue models
Outlier detection for high dimensional data using the Comedian approach
A survey on unsupervised outlier detection in high‐dimensional numerical data
Distance-based outlier detection for high dimension, low sample size data
Geometric consistency of principal component scores for high‐dimensional mixture models and its application

This page was built for publication: Test for high-dimensional outliers with principal component analysis