Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization

Abstract

In the context of Nadaraya-Watson kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection. In this paper, a firefly optimization algorithm method is proposed to choose the smoothing parameter in Nadaraya-Watson kernel nonparametric regression. The proposed method will efficiently help to find the best smoothing parameter with a high prediction. The proposed method is compared with four famous methods. The experimental results comprehensively demonstrate the superiority of the proposed method in terms of prediction capability.

Keywords

• nadaraya-watson estimator
• firefly optimization algorithm
• smoothing parameter selection