Optimum adaptive bandwidth selection method of local fitting in kernel regression analysis for non-uniform data

Selection of a global bandwidth is commonly used in kernel regression. On the other hand, the pointwise choice of a local bandwidth can lead to better results in kernel regression because it has direct effect on smoothing the signal. These smoothing bandwidths affect the filtering capacity of all signals and systems. It demonstrates a greater adaptability to a variety of analysis ranging from one-dimensional to multidimensional problems, as well as different classes of engineering branches of human–machine interactions. In this paper, we propose a new method called optimum adaptive local bandwidth selection method (OALB), which depends on the bias-variance optimization ratio. It is based on Stankovic optimization of the bias-variance of the signal (Stanković in IEEE Trans Signal Process 52:1228–1234, 2004). The bandwidth is calculated independently for every point based on the intersection of confidence interval (ICI).