Is Lasso better than Ridge?
The only difference from Ridge regression is that the regularization term is in absolute value. Lasso method overcomes the disadvantage of Ridge regression by not only punishing high values of the coefficients β but actually setting them to zero if they are not relevant.
Should I use Lasso or ridge regression?
Lasso tends to do well if there are a small number of significant parameters and the others are close to zero (ergo: when only a few predictors actually influence the response). Ridge works well if there are many large parameters of about the same value (ergo: when most predictors impact the response).
Is the adaptive group LASSO method the same as Lasso?
Hence, it is expected that gLasso in Yuan and Lin (2006) suffers from the estimation inefficiency and selection inconsistency in the same way as lasso. As a remedy, we propose the adaptive group lasso (agLasso) method.
Which is more efficient oracle or adaptive lasso?
As a remedy, we propose the adaptive group lasso (agLasso) method. It is similar to adaptive lasso but has the capability to select variables in a grouped manner. We show theoretically that the proposed agLasso estimator is able to identify the true model consistently, and the resulting estimator is as efficient as oracle.
Which is the most important feature of Lasso?
Since its first proposal by Tibshirani (1996), the least absolute shrinkage and selection operator (lasso) has generated much interest in the statistical literature ( Fu, 1998, Knight and Fu, 2000, Fan and Li, 2001, Efron et al., 2004 ). The key strength of lasso lies in its ability to do simultaneous parameter estimation and variable selection.
Why does Lasso apply the same shrinkage for all regression coefficients?
The major reason accounting for such a deficiency is that lasso applies the same amount of shrinkage for each regression coefficient. As a simple solution, Zou (2006) modified the lasso penalty so that different amounts of shrinkage are allowed for different regression coefficients.