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Estimation Method Options
the l1 penalty, which penalizes the sum of the absolute values of the regression coefficients
the l2 penalty, which penalizes the sum of the squares of the regression coefficients
(Available only when the specified Distribution is Normal and the No Intercept option is not selected.) Computes parameter estimates by applying an l1 penalty using a linear programming approach. See Candes and Tao (2007). The Dantzig Selector is useful for analyzing the results of designed experiments. For orthogonal problems, the Dantzig Selector and Lasso give identical results. For more information, see Dantzig Selector.
Computes parameter estimates by applying an l1 penalty. Due to the l1 penalty, some coefficients can be estimated as zero. Thus, variable selection is performed as part of the fitting procedure. In the ordinary Lasso, all coefficients are equally penalized.
Computes parameter estimates by penalizing a weighted sum of the absolute values of the regression coefficients. The weights in the l1 penalty are determined by the data in such as way as to guarantee the oracle property (Zou 2006). This option uses the MLEs to weight the l1 penalty. MLEs cannot be computed when the number of predictors exceeds the number of observations or when there are strict linear dependencies among the predictors. If MLEs for the regression parameters cannot be computed, a generalized inverse solution or a ridge solution is used for the l1 penalty weights. See Adaptive Methods.
Computes parameter estimates by applying both an l1 penalty and an l2 penalty. The l1 penalty ensures that variable selection is performed. The l2 penalty improves predictive ability by shrinking the coefficients as ridge does.
Computes parameter estimates using an adaptive l1 penalty as well as an l2 penalty. This option uses the MLEs to weight the l1 penalty. MLEs cannot be computed when the number of predictors exceeds the number of observations or when there are strict linear dependencies among the predictors. If MLEs for the regression parameters cannot be computed, a generalized inverse solution or a ridge solution is used for the l1 penalty weights. You can set a value for the Elastic Net Alpha in the Advanced Controls panel. See Adaptive Methods.
Computes parameter estimates using ridge regression. Ridge regression is a biased regression technique that applies an l2 penalty and does not result in zero parameter estimates. It is useful when you want to retain all predictors in your model. For more details, see Ridge Regression.

Help created on 3/19/2020