山下 遥

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

The analysis of binary (0 or 1) data requires an analysis method whose objects are realizations. Yamashita and Suzuki (to appear) proposed principal points for binary distributions based on the concept of principal points, defined by Flury (1990). Ideally, when we search for the binary principal points, all combinations of the k-principal points should be considered; however, this problem cannot be solved in a straightforward manner because the number of combinations increases exponentially when the number of the variables increases. In this paper, we propose three heuristic methods for approximating principal points for binary distributions. The results indicate that our method enables us to find approximated principal points and summarize a binary distribution using the points.