The "least absolute shrinkage and selection operator" ('lasso') has been widely used in regression shrinkage and selection. We extend its application to the regression model with autoregressive errors. Two types of lasso estimators are carefully studied. The first is similar to the traditional...

We obtain the residual information criterion RIC, a selection criterion based on the residual log-likelihood, for regression models including classical regression models, Box-Cox transformation models, weighted regression models and regression models with autoregressive moving average errors. We...