Classification is an important task when data are curves. Recently, the notion of statistical depth has been extended to deal with functional observations. In this paper, we propose robust procedures based on the concept of depth to classify curves. These techniques are applied to a real data example. An extensive simulation study with contaminated models illustrates the good robustness properties of these depth-based classification methods.
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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 and Swedish Statistical Association, vol. 30(3), pages 581-595.
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Anil K. Ghosh & Probal Chaudhuri, 2005.
"On Maximum Depth and Related Classifiers,"
Scandinavian Journal of Statistics,
Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association and Swedish Statistical Association, vol. 32(2), pages 327-350.
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