Influence in principal components analysis
Copyright policy )Influence in principal components analysis
To detect outliers in data analyzed for principal components, three types of influence functions are derived based on perturbation parameters. Influence of these functions on the eigenvalues and eigenvectors of the covariance matrix is examined. The functions are compared among themselves and are contrasted with the regression case.
- University of Warwick United Kingdom
covariance matrix, outliers, eigenvalues, eigenvectors, Factor analysis and principal components; correspondence analysis, linear regression, Robustness and adaptive procedures (parametric inference), principal components, influence functions, perturbation parameters
covariance matrix, outliers, eigenvalues, eigenvectors, Factor analysis and principal components; correspondence analysis, linear regression, Robustness and adaptive procedures (parametric inference), principal components, influence functions, perturbation parameters
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