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چکیده
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A classification problem with a functional predictor is studied, and it is suggested to use a multinomial functional regression (MFR) model for the analysis. The discrete wavelet trans- form and LASSO penalization are combined for estimation, and the fitted model is used for classification of new curves with unknown class membership. The MFR approach is ap- plied to two datasets, one regarding lameness detection for horses and another regarding speech recognition. In the applications, as well as in a simulation study, the performance of the MFR approach is compared to that of other methods for supervised classification of functional data, and MFR performs as well or better than the other methods.
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