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Classification of functional data: a weighted distance approach

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  • Alonso Fernández, Andrés Modesto
  • Casado, David
  • Romo, Juan

Abstract

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.

Suggested Citation

  • Alonso Fernández, Andrés Modesto & 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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    References listed on IDEAS

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    1. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1683, August.
    2. 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.
    3. Cuesta-Albertos, Juan Antonio & Fraiman, Ricardo, 2007. "Impartial trimmed k-means for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4864-4877, June.
    4. 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.
    5. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1684, August.
    6. Nerini, David & Ghattas, Badih, 2007. "Classifying densities using functional regression trees: Applications in oceanology," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4984-4993, June.
    7. 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.
    8. Dabo-Niang, Sophie & Ferraty, Frederic & Vieu, Philippe, 2007. "On the using of modal curves for radar waveforms classification," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4878-4890, June.
    9. Li, Bin & Yu, Qingzhao, 2008. "Classification of functional data: A segmentation approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4790-4800, June.
    10. 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, September.
    11. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
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    Keywords

    Discriminant analysis;

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