Communications for Statistical Applications and Methods

  • 제12권1호
  • /
  • Pages.39-46
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  • 2005
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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A Robust Estimator in Multivariate Regression Using Least Quartile Difference

  • Jung Kang-Mo (Department of Informatics & Statistics, Kunsan National University)
  • 발행 : 2005.04.01
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초록

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.

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참고문헌

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