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A Robust Estimator in Multivariate Regression Using Least Quartile Difference
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 Title & Authors
A Robust Estimator in Multivariate Regression Using Least Quartile Difference
Jung Kang-Mo;
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We propose an equivariant and robust estimator in multivariate regression model based on the least quartile difference (LQD) estimator in univariate regression. We call this estimator as the multivariate least quartile difference (MLQD) estimator. The MLQD estimator considers correlations among response variables and it can be shown that the proposed estimator has the appropriate equivariance properties defined in multivariate regressions. The MLQD estimator has high breakdown point as does the univariate LQD estimator. We develop an algorithm for MLQD estimate. Simulations are performed to compare the efficiencies of MLQD estimate with coordinatewise LQD estimate and the multivariate least trimmed squares estimate.
Breakdown point;Equivariance;Least quartile difference estimator;Multivariate regression;Outliers;
 Cited by
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