[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CKSS.2003.10.3.1037

An Equivariant and Robust Estimator in Multivariate Regression Based on Least Trimmed Squares

Jung, Kang-Mo (Department of Informatics & Statistics, Kunsan National University)

Publication Information

Communications for Statistical Applications and Methods / v.10, no.3, 2003 , pp. 1037-1046 More about this Journal

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;

Citations & Related Records

Reference

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