maximization of log-likelihood with Cp, AIC, and BIC penalties, bootstrap and cross-validation error estimation, and coefficient …

The Bayesian information criterion (BIC) is one of the most popular criteria for model selection in finite mixture … called hierarchical BIC (HBIC) is proposed which penalizes the component complexity only using its local sample size and … bound when sample size is large and the widely used BIC is a less accurate approximation. An empirical study is conducted to …

of parsimonious models. Current wisdom suggests the Bayesian information criterion (BIC) for mixture model selection …. However, the BIC has well-known limitations, including a tendency to overestimate the number of components as well as a … this paper, a LASSO-penalized BIC (LPBIC) is introduced to overcome this problem. This approach is illustrated based on …

-sample-corrected AIC and BIC on the M1 and M3 Competition datasets. Weighted forecast combinations perform better than forecasts selected …