In Markov-switching regression models, we use Kullback-Leibler (KL) divergence between the true and candidate models to select the number of states and variables simultaneously. In applying Akaike information criterion (AIC), which is an estimate of KL divergence, we find that AIC retains too...

An improved AIC-based criterion is derived for model selection in general smoothing-basedmodeling, including semiparametric models and additive models. Examples areprovided of applications to goodness-of-fit, smoothing parameter and variable selectionin an additive model and semiparametric...