Wavelet-Based Estimation of Generalized Discriminant Functions

Author(s)

Montoril, Michel H. ^[1] ; Chang, Woojin ^[2] ; Vidakovic, Brani ^[3]

Affiliation

^[1] Univ Fed Juiz de Fora, Dept Stat, Juiz De Fora - Brazil

^[2] Georgia Inst Technol, H Milton Stewart Sch Ind & Syst Engn, Atlanta, GA 30332 - USA

^[3] Seoul Natl Univ, Dept Ind Engn, Seoul - South Korea

• Document type: Journal article
• Source: SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS; v. 81, n. 2, p. 318-349, DEC 2019.
• Web of Science Citations: 0

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. (AU)

• FAPESP's process: 13/09035-1 - Regression models in functional data analysis
• Grantee: Michel Helcias Montoril
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 13/21273-5 - Estimation of semi-functional linear models by wavelets
• Grantee: Michel Helcias Montoril
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor