Abstract
Multivariate analysis is generically known as a collection of statistical notion, methods and theory on multivariate data of several variables. In general, there are two directions in the course of research in multivariate analysis. One lays stress on the research of statistical inference, assuming probability distributions for multivariate data. The other is free from the assumptions of probability distributions, and lays stress on reduced representations, etc. of multivariate data. In the latter case, various methods have been proposed for qualitative or multi-way data as well as conventional multivariate data. The former is called multivariate statistical analysis, and the latter is called multivariate data analysis. Strictly speaking, these two directions cannot be discriminated. On the other side, it is natural that one should create a system of multivariate statistical methods, making up for the two directions each other. However, at the present time great progress is being made in two directions, and two directions are rather independently developing. From these reasons we first divide multivariate statistical methods into methods related to statistical inference and those related to multivariate data analysis, and discuss current perspectives and future developments in some topics of methods. The contributor of Part I: Multivariate Statistical Inference is Yasunori Fujikoshi, and the contributor of Part II: Multivariate Data Analysis is Haruo Yanai. In the last part of Part III we list books and proceedings on multivariate analysis, discriminating these into various areas. The contents of this paper are as follows:
