Comparison of model selection methods for the estimation of principal points for a multivariate binary distribution
Haruka Yamashita, Shun Matsuura, Hideo Suzuki · Total Quality Science · 2015
Recently, a parametric estimation method for principal points for a multivariate binary distribution using a log-linear model has been proposed, and Akaike information criterion (AIC) has been applied to model selection for log-linear model. This paper compares three model selection methods based on AIC, Bayesian information criterion (BIC), and the likelihood ratio test (LRT) for estimating principal points for a multivariate binary distribution. The performances of the model selection methods are shown through numerical simulation studies