Orthonormal Canonical Correlation Analysis

Abstract

Abstract Complex managerial problems are usually described by datasets with multiple variables, and in lack of a theoretical model, the data structures can be found by special multivariate statistical techniques. For two datasets, the canonical correlation analysis and its robust version are known as good working research tools. This paper presents their further development via the orthonormal approximation of data matrices which corresponds to using singular value decomposition in the canonical correlations. The features of the new method are described and applications considered. This type of multivariate analysis is useful for solving various practical problems of applied statistics requiring operating with two data sets, and can be helpful in managerial estimations and decision making.