Autor(es)

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

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS; v. 81, n. 2, p. 318-349, DEC 2019.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 13/09035-1 - Modelos de regressão para dados funcionais
• Beneficiário: Michel Helcias Montoril
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 13/21273-5 - Estimação de modelos de regressão linear semifuncionais via ondaletas
• Beneficiário: Michel Helcias Montoril
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado