Comparison of model selection methods for the estimation of principal points for a multivariate binary distribution

Haruka Yamashita, Shun Matsuura, Hideo Suzuki
2015 Total Quality Science  
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
doi:10.17929/tqs.1.22 fatcat:mmdq6y75ezbupo4onzuxe4xu7m