Exploratory Methods for Joint Distribution Valued Data and Their Application
Kazuto Igarashi, Hiroyuki Minami, Masahiro Mizuta · Communications for Statistical Applications and Methods · 2015
In this paper, we propose hierarchical cluster analysis and multidimensional scaling for joint distribution valued data.Information technology is increasing the necessity of statistical methods for large and complex data.Symbolic Data Analysis (SDA) is an attractive framework for the data.In SDA, target objects are typically represented by aggregated data.Most methods on SDA deal with objects represented as intervals and histograms.However, those methods cannot consider information among variables including correlation.In addition, objects represented as a joint distribution can contain information among variables.Therefore, we focus on methods for joint distribution valued data.We expanded the two well-known exploratory methods using the dissimilarities adopted Hall Type relative projection index among joint distribution valued data.We show a simulation study and an actual example of proposed methods.