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A Criterion for the Selection of Principal Components in the Robust Principal Component Regression
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 Title & Authors
A Criterion for the Selection of Principal Components in the Robust Principal Component Regression
Kim, Bu-Yong;
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 Abstract
Robust principal components regression is suggested to deal with both the multicollinearity and outlier problem. A main aspect of the robust principal components regression is the selection of an optimal set of principal components. Instead of the eigenvalue of the sample covariance matrix, a selection criterion is developed based on the condition index of the minimum volume ellipsoid estimator which is highly robust against leverage points. In addition, the least trimmed squares estimation is employed to cope with regression outliers. Monte Carlo simulation results indicate that the proposed criterion is superior to existing ones.
 Keywords
Multicollinearity;outlier;robust principal components regression;minimum volume ellipsoid estimator;condition index;least trimmed squares estimation;
 Language
Korean
 Cited by
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