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An Equivariant and Robust Estimator in Multivariate Regression Based on Least Trimmed Squares
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 Title & Authors
An Equivariant and Robust Estimator in Multivariate Regression Based on Least Trimmed Squares
Jung, Kang-Mo;
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 Abstract
We propose an equivariant and robust estimator in multivariate regression model based on the least trimmed squares (LTS) estimator in univariate regression. We call this estimator as multivariate least trimmed squares (MLTS) estimator. The MLTS estimator considers correlations among response variables and it can be shown that the proposed estimator has the appropriate equivariance properties defined in multivariate regression. The MLTS estimator has high breakdown point as does LTS estimator in univariate case. We develop an algorithm for MLTS estimate. Simulation are performed to compare the efficiencies of MLTS estimate with coordinatewise LTS estimate and a numerical example is given to illustrate the effectiveness of MLTS estimate in multivariate regression.
 Keywords
Breakdown point;Equivariance;Least trimmed squares estimator;Multivariate regression;Outliers;
 Language
English
 Cited by
1.
A Robust Estimator in Multivariate Regression Using Least Quartile Difference,;

Communications for Statistical Applications and Methods, 2005. vol.12. 1, pp.39-46 crossref(new window)
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