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Classification of functional data: a weighted distance approach

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  • Andrés M. Alonso

    ()

  • David Casado

    ()

  • Juan Romo

    ()

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    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.

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    File URL: http://e-archivo.uc3m.es/bitstream/10016/8327/1/ws093915.pdf
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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws093915.

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    Date of creation: Jul 2009
    Date of revision:
    Handle: RePEc:cte:wsrepe:ws093915

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    Related research

    Keywords: Discriminant analysis; Functional data; Weighted distances;

    This paper has been announced in the following NEP Reports:

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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, vol. 58(3), pages 619-633, September.
    2. 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, 08.
    3. Li, Bin & Yu, Qingzhao, 2008. "Classification of functional data: A segmentation approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4790-4800, June.
    4. 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.
    5. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
    6. Roderick McDonald & Yukihiko Torii & Shizuhiko Nishisato, 1979. "Some results on proper eigenvalues and eigenvectors with applications to scaling," Psychometrika, Springer, vol. 44(2), pages 211-227, June.
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