Weighted Least Absolute Deviation Lasso Estimator
نویسندگان
چکیده
منابع مشابه
Weighted Least Absolute Deviation Lasso Estimator
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 L1 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 deviatio...
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The least absolute deviation or L1 method is a widely known alternative to the classical least squares or L2 method for statistical analysis of linear regression models. Instead of minimizing the sum of squared errors, it minimizes the sum of absolute values of errors. Despite its long history and many ground-breaking works (cf. Portnoy and Koenker (1997) and references therein), the former has...
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Orthogonal least trimmed absolute deviation (OLTAD) estimator of the multiple linear errors-in-variables (EIV) model is presented. We show that the OLTAD estimator has the high breakdown point and appropriate properties. A new decimal-integer-coded genetic algorithm(DICGA) and Fast-OLTAD method for solving OLTAD estimators are also proposed. Computational experiments of the OLTAD estimator of t...
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ژورنال
عنوان ژورنال: Communications for Statistical Applications and Methods
سال: 2011
ISSN: 2287-7843
DOI: 10.5351/ckss.2011.18.6.733