Multinomial functional regression with wavelets and LASSO penalization

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• 作者：Seyed Nourollah Mousavi^a ; ^{n-mousavi@araku.ac.ir} ; Helle Sø ; rensen ; ^b ; ^{helle@math.ku.dk}
• 关键词：Discrete wavelet transform ; Functional predictor ; Supervised classification ; Lameness data for horses ; Phoneme data
• 刊名：Econometrics and Statistics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：1
• 期：Complete
• 页码：150-166
• 全文大小：2612 K
• 卷排序：1

文摘

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 transform 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 applied 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.