Bayesian Lasso R. C r delta the alpha shape parameter and β rate parameter to the gamma distribution prior G rdelta for the λ 2 parameter under the lasso model. I Although more computationally intensive the Bayesian Lasso is easy to implement and automatically provides interval estimates for all parameters including the error variance.
A default of NULL generates appropriate non-informative values depending on the nature of the regression. The Bayesian Lasso estimates appear to be a compromise between the Lasso and ridge regression estimates. Casella Normal-Gamma from Griffin.
Let Y Xfle where the observed data on the predictors are contained in the n x p 1 matrix X and the observed data on the dependent variable are contained in the n-vector Y.
Aug 02 2020 On page 227 the authors provide a Bayesian point of view to both ridge and LASSO regression. The Bayesian Lasso estimates seem to be a compromise between the Lasso and ridge regression estimates. In rstanarm setting stan_glms prior argument to laplace uses a fixed penalty whereas lasso will tune the penalty as a hyperparameter as stated in this post. Bayesian framework and selection of prior distributions We adopt a variance-inflation model for outliers as follows.
