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

Zheng Tian, Chun Hui Zhao, Zhan Shou Chen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)351-357
Number of pages7
JournalKongzhi Lilun Yu Yingyong/Control Theory and Applications
Volume28
Issue number3
StatePublished - Mar 2011

Keywords

  • Bootstrap test
  • Kernel estimation
  • Mean function
  • Nonparametric model
  • Structural break

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