Weighted Least Absolute Deviation Lasso Estimator

Title & Authors
Weighted Least Absolute Deviation Lasso Estimator
Jung, Kang-Mo;

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 $\small{L_1}$ 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.
Keywords
Heavy-tailed errors;Lasso;leverage points;outliers;robust estimator;weight least absolute deviation;
Language
English
Cited by
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Weighted Least Absolute Deviation Regression Estimator with the SCAD Function,;

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2.
Weighted Support Vector Machines with the SCAD Penalty,;

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3.
Weighted L1 Multicategory Support Vector Machines for Unbalanced Cases,;

Journal of the Korean Data Analysis Society, 2015. vol.17. 3A, pp.1175-1184
1.
Weighted Support Vector Machines with the SCAD Penalty, Communications for Statistical Applications and Methods, 2013, 20, 6, 481
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