Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Weighted Least Absolute Deviation Lasso Estimator

  • Jung, Kang-Mo (Department of Informatics & Statistics, Kunsan National University)
  • Received : 20110900
  • Accepted : 20111000
  • Published : 2011.11.30
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Abstract

The linear absolute shrinkage and selection operator(Lasso) method improves the low prediction accuracy and poor interpretation of the ordinary least squares(OLS) estimate through the use of $L_1$ regularization on the regression coefficients. However, the Lasso is not robust to outliers, because the Lasso method minimizes the sum of squared residual errors. Even though the least absolute deviation(LAD) estimator is an alternative to the OLS estimate, it is sensitive to leverage points. We propose a robust Lasso estimator that is not sensitive to outliers, heavy-tailed errors or leverage points.

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References

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