DISSIMILARITY AND RELATED METHODS FOR FUNCTIONAL DATA

• TOKUSHIGE Shuichi

  Graduate School of Science and Engineering, Kagoshima University

• INADA Koichi

  Department of Mathematics and Computer Science, Kagoshima University

• YADOHISA Hiroshi

  Department of Mathematics and Computer Science, Kagoshima University

Abstract

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.

Journal

• Journal of the Japanese Society of Computational Statistics

  Journal of the Japanese Society of Computational Statistics 15 (2), 319-326, 2003-06-01

  Japanese Society of Computational Statistics

Keywords

• Dissimilarity
• Distortion
• Hierarchical clustering
• Registration
• Smoothing

Details

• CRID: 1572543026791460992
• NII Article ID: 110001235185
• NII Book ID: AA10823693
• ISSN: 09152350
• Text Lang: en
• Data Source:
  • CiNii Articles