The Effect of Outlier on Lasso Estimators and Regressions
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Abstract
Lasso regression (Least Absolute Shrinkage and Selection Operator) dependent on reducing shrinkage. This kind regression deals with cases in which the explained variables have a multicollinearity problem between them and in models include a large number of explained variables with the goal is to focus on the variables that have the most effect on the dependent variable. In this research Lasso regression were presented with deferent (sample size, number of explained variables and number of outliers) to show its effect on lasso and Bayesian lasso regression. Numerical results showed that Lasso estimator was affected by each of the sample size, outlier's ratios and regression method. Other methods, such as shrinkage ridge and Bayesian ridge methods can be used for comparison with the assumed methods
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