Detection and applications of structural breaks of mean function in nonparametric regression models

The detection of parametric change is transformed into the detection of structural breaks of mean function in the nonparametric models. For the residual cumulative sum(CUSUM) test becomes invalid when the long rang average of jump of the mean function is zero, a new statistic is built based on the kernel estimation of the mean function, and the limiting distributions of null hypothesis and alternative hypothesis are obtained. A Bootstrap procedure is proposed and the consistency of the test is also proved. Finally, simulation and real data analysis are performed to investigate the finite sample properties of our approach. Results show that our method is more powerful than methods proposed in reference.