Abstract
The Lasso is a very popular model selection tool (Tibshirani, 1996). It is a regularized linear regression method what shrinks the estimated coefficients and selects variables at the same time. Although the Lasso is widely used, the criteria of model selection are still under development. In this work, we review and compare the performance between several existing model selection criteria, including Cross-validation by Tibshirani (1996), modified C_p, AIC and BIC by Zou et al. (2007), Covariance test by Lockhart et al. (2014) and Exact Inference test by Lee et al. (2014). Also, we propose an alternative criterion and provide some solutions to problems in the Covariance test and the Lee's test.