State-domain change point detection for nonlinear time series regression

Change point detection in time series has attracted substantial interest, but most of the existing results have been focused on detecting change points domain. This paper considers situation where nonlinear potential state We apply a density-weighted anti-symmetric kernel function to domain and therefore propose nonparametric procedure test existence points. When is affirmative, we further introduce an algorithm estimate number together with their locations. Theoretical proposed estimation procedures are given real dataset used illustrate our methods.