K-Nearest Neighbor Estimation of Functional Nonparametric Regression Model under NA Samples

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