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抄録
Functional data analysis, as proposed by Ramsay (1982), has been attracting many researchers. The most popular approach in recent studies of functional data has been to extend the statistical methods for usual data to functional data. Ramsay and Silverman (1997), for example, proposed regression analysis, principal component analysis, canonical correlation analysis, linear models, etc. for functional data. In this paper, we propose several dissimilarities of functional data. We discuss comparison of these dissimilarities by using the cophenetic correlation coefficient and the sum of squares. Our concern is the effect of dissimilarity on the result of analysis that is applied to dissimilarity data; e.g., cluster analysis.
