It has been shown that Akaike information criterion (AIC)-type criteria are asymptotically efficient selectors of the tuning parameter in nonconcave penalized regression methods under the assumption that the population variance is known or that a consistent estimator is available. We relax this...

We develop a small sample criterion (L1cAIC) for the selection of least absolute deviations regression models. In contrast to AIC (Akaike, 1973), L1cAIC provides an exactly unbiased estimator for the expected Kullback--Leibler information, assuming that the errors have a double exponential...

We obtain the asymptotic distributions of the linear regression models selected by the Akaike Information Criterion (AIC) and the Schwarz Information Criterion (BIC), in the presence of unsuspected serial correlations. We assume that the models are fitted by ordinary least squares to a data set...