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Classification of functional data: a weighted distance approach


  • Alonso, Andrés M.
  • Casado, David
  • Romo, Juan


A popular approach for classifying functional data is based on the distances from the function or its derivatives to group representative (usually the mean) functions or their derivatives. In this paper, we propose using a combination of those distances. Simulation studies show that our procedure performs very well, resulting in smaller testing classication errors. Applications to real data show that our procedure performs as well as –and in some cases better than– other classication methods.

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  • Alonso, Andrés M. & Casado, David & Romo, Juan, 2009. "Classification of functional data: a weighted distance approach," DES - Working Papers. Statistics and Econometrics. WS ws093915, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:ws093915

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    1. C. Abraham & G. Biau & B. Cadre, 2006. "On the Kernel Rule for Function Classification," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(3), pages 619-633, September.
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    4. Roderick McDonald & Yukihiko Torii & Shizuhiko Nishisato, 1979. "Some results on proper eigenvalues and eigenvectors with applications to scaling," Psychometrika, Springer;The Psychometric Society, vol. 44(2), pages 211-227, June.
    5. Li, Bin & Yu, Qingzhao, 2008. "Classification of functional data: A segmentation approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4790-4800, June.
    6. C. Abraham & P. A. Cornillon & E. Matzner-Løber & N. Molinari, 2003. "Unsupervised Curve Clustering using B-Splines," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(3), pages 581-595.
    7. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
    8. James G.M. & Sugar C.A., 2003. "Clustering for Sparsely Sampled Functional Data," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 397-408, January.
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    Discriminant analysis;

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