observations. It does not require predictions or observations to meet the requirements of interval or ratio measurement scales. In …Most traditional strategies of assessing the fit between a simulation's set of predictions (outputs) and a set of … relevant observations rely either on visual inspection or squared distances among averages. Here we introduce an alternative …

term. We derive the asymptotic theory of the maximum likelihood estimators and show they are consistent and asymptotically … comparisons of a consistent version of our $C_{p}$ statistic with BIC and a generalized RIC show that our statistic has superior … about the choice of model. On the way we introduce a new version of AIC for regression models, show that it estimates a …

In this paper, Mallows'(1973) Cp criterion, Akaike's (1973) AIC, Hurvich and Tsai's (1989) corrected AIC and the BIC of … model selection criteria, the BIC appears to be most successful in reducing MSE, and Cp in reducing bias. We also observe …

, BIC, AIC and general-tospecific. We propose a new method and show that it is well behaved compared to existing methods. … variables is large compared to the number of observations. This makes it infeasible to condition on all variables in order to …

studies, in which the bias-corrected AIC is shown to outperform the traditional AIC and BIC statistics and the RCM test …, which includes maximum likelihood estimation and inference, model selection, and goodness-of-fit test. Particularly in AR … models a bias-corrected AIC statistic is derived for the order selection, while a randomized conditional moment (RCM) test is …

standard information criteria such as AIC and BIC, AICcond is focused on the classification task, since it attempts to measure …

In this paper, Mallows'(1973) Cp criterion, Akaike's (1973) AIC, Hurvich and Tsai's (1989) corrected AIC and the BIC of … model selection criteria, the BIC appears to be most successful in reducing MSE, and Cp in reducing bias. We also observe …