Wavelet-Based Estimation of Generalized Discriminant Functions

Michel H. Montoril (), Woojin Chang and Brani Vidakovic
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Michel H. Montoril: Federal University of Juiz de Fora
Woojin Chang: Seoul National University
Brani Vidakovic: Georgia Institute of Technology

Sankhya B: The Indian Journal of Statistics, 2019, vol. 81, issue 2, No 7, 318-349

Abstract: Abstract In this work we propose a wavelet-based classifier method for binary classification. Basically, based on a training data set, we provide a classifier rule with minimum mean square error. Under mild assumptions, we present asymptotic results that provide the rates of convergence of our method compared to the Bayes classifier, ensuring universal consistency and strong universal consistency. Furthermore, in order to evaluate the performance of the proposed methodology for finite samples, we illustrate the approach using Monte Carlo simulations and real data set applications. The performance of the proposed methodology is compared with other classification methods widely used in the literature: support vector machine and logistic regression model. Numerical results showed a very competitive performance of the new wavelet-based classifier.

Keywords: Generalized classification; wavelet estimation; nonparametric regression; Primary: 62G05; 62G08; 62G20; Secondary: 62P30; 62P10 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s13571-018-0158-1

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