K-nearest neighbor estimation of functional nonparametric regression model under NA samples

Abstract

Copyright © 2022 by the authors. Functional data, which provides information about curves, surfaces or anything else varying over a continuum, has become a commonly encountered type of data. While k-Nearest Neighbor (kNN) method, as a nonparametric method, has become one of the most popular supervised machine learning algorithms being used to solve both classification and regression problems, this paper is devoted to the k-nearest neighbor (kNN) estimators of the non-parametric functional regression model whenthe observed variables take values from Negatively Associated (NA) sequences. The consistent and complete convergence rate for the proposed kNN estimator are first provided. Then numerical assessments, including simulation study and real data analysis, are conducted to evaluate the performance of the proposed method and compare it with standard nonparametric kernel approach.