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• W2950144238 abstract "A fast nonparametric procedure for classifying functional data is introduced. It consists of a two-step transformation of the original data plus a classifier operating on a low-dimensional hypercube. The functional data are first mapped into a finite-dimensional location-slope space and then transformed by a multivariate depth function into the $DD$-plot, which is a subset of the unit hypercube. This transformation yields a new notion of depth for functional data. Three alternative depth functions are employed for this, as well as two rules for the final classification on $[0,1]^q$. The resulting classifier has to be cross-validated over a small range of parameters only, which is restricted by a Vapnik-Cervonenkis bound. The entire methodology does not involve smoothing techniques, is completely nonparametric and allows to achieve Bayes optimality under standard distributional settings. It is robust, efficiently computable, and has been implemented in an R environment. Applicability of the new approach is demonstrated by simulations as well as a benchmark study." @default.
• W2950144238 created "2019-06-27" @default.
• W2950144238 creator A5041121259 @default.
• W2950144238 creator A5044201377 @default.
• W2950144238 date "2014-03-05" @default.
• W2950144238 modified "2023-09-27" @default.
• W2950144238 title "Fast DD-classification of functional data" @default.
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