Generalized spherical principal component analysis

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Robustness and adaptive procedures (parametric inference) (62F35)

Cites Work

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On the eigenvalues of the spatial sign covariance matrix in more than two dimensions
Central limit theorem and influence function for the MCD estimators at general multivariate distributions
Robustifying principal component analysis with spatial sign vectors
High breakdown estimators for principal components: the projection-pursuit approach revis\-ited
Asymptotic theory for robust principal components
Asymptotic behaviour of S-estimates of multivariate location parameters and dispersion matrices
The asymptotics of Rousseeuw's minimum volume ellipsoid estimator
Robust principal component analysis for functional data. (With comments)
Asymptotics for the minimum covariance determinant estimator
BACON: blocked adaptive computationally efficient outlier nominators.
The spatial sign covariance operator: asymptotic results and applications
Some robust estimates of principal components
Influence function and efficiency of the minimum covariance determinant scatter matrix estimator
Asymptotic and bootstrap tests for subspace dimension
The spatial sign covariance matrix with unknown location
Law of log determinant of sample covariance matrix and optimal estimation of differential entropy for high-dimensional Gaussian distributions
A generalized spatial sign covariance matrix
Shape bias of robust covariance estimators: an empirical study
The \(k\)-step spatial sign covariance matrix
Eigenvalues of large sample covariance matrices of spiked population models
Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
The asymptotic inadmissibility of the spatial sign covariance matrix for elliptically symmetric distributions
Least Median of Squares Regression
A Measure of Directional Outlyingness With Applications to Image Data and Video
Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo
Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation
Principal component analysis based on robust estimators of the covariance or correlation matrix: influence functions and efficiencies
Projection pursuit in high dimensions
On Consistency and Sparsity for Principal Components Analysis in High Dimensions

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