A1269
Title: Enhancing performance in functional nonparametric regression and classification using weighted pseudo-metrics
Authors: Kwo Lik Lax Chan - Universita degli Studi del Piemonte Orientale (Italy) [presenting]
Aldo Goia - University of Eastern Piedmont Amedeo Avogadro (Italy)
Abstract: The selection of a pseudo-metric is a key issue in functional data analysis, influencing the performance of both nonparametric regression and classification methods. A class of weighted pseudo-metrics designed to better capture relevant features of functional covariates is introduced. Performance is evaluated via Monte Carlo simulations and real data analysis.
