In social network analysis, many estimation methods have been developed over the past three decades. Due to the computational complexity for analyzing large-scale social network data, however, those methods cannot be applied effectively. On the other hand, the structure of large-scale network...

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...

The mean-variance theory of Markowitz (1952) indicates that large investment portfolios naturally provide better risk diversi cation than small ones. However, due to parameter estimation errors, one may find ambiguous results in practice. Hence, it is essential to identify relevant stocks to...

In finite mixture regression models, we generalize the application of the least absolute shrinkage and selection operator (LASSO) to obtain MR-Lasso, which incorporates both mixture and regression penalties. Because MR-Lasso jointly penalizes both regression coeficients and mixture components,...

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...