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

Abstract

Abstract: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

Principal simple linear regression

“…[12] present optimal estimators for minimizing expected mean squared distance. Yamashita [19] contributes a doctoral dissertation studying principal points for a multivariate binary distribution, and the comparison of model selection methods for their estimation is presented by [20]. The utility of principal points extends to diverse applications, as seen in the work of [9], who apply them to partition functional gene expression data.…”