Open AccessA Comparative Study of Nearest Neighbor Regression and Nadaraya Watson Regression
Author(s) -
Sarwar A. Hamad,
Kawa S. Mohamed Ali
Publication year - 2021
Publication title -
academic journal of nawroz university
Language(s) - English
Resource type - Journals
ISSN - 2520-789X
DOI - 10.25007/ajnu.v10n2a505
Subject(s) - estimator , k nearest neighbors algorithm , kernel regression , smoothing , regression , mathematics , kernel (algebra) , kernel smoother , nonparametric regression , statistics , regression analysis , computer science , artificial intelligence , kernel method , support vector machine , radial basis function kernel , combinatorics
Two non-parametric statistical methods are studied in this work. These are the nearest neighbor regression and the Nadaraya Watson kernel smoothing technique. We have proven that under a precise circumstance, the nearest neighborhood estimator and the Nadaraya Watson smoothing produce a smoothed data with a same error level, which means they have the same performance. Another result of the paper is that nearest neighborhood estimator performs better locally, but it graphically shows a weakness point when a large data set is considered on a global scale.