Wavelet estimation of a regression function with a sharp change-point in heavy-tailed noise

This paper considers the problem of a wavelet method to estimate a sharp change point and a nonparametric regression function under random design, whose noise is heavy tailed infinite-varianced process. By using two-step method, we propose a truncation estimator of the change point, which can weaken...

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Bibliographic Details
Published inProceedings of the 31st Chinese Control Conference pp. 737 - 743
Main Authors Zhang Baoshang, Li Xiao-Yan, Tian Zheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2012
Subjects
Change point
Educational institutions
Electronic mail
Electrooptical waveguides
Estimation
Infinite variance process
Laboratories
Noise
Nonparametric regression model
Random design
Truncation estimator
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Summary:This paper considers the problem of a wavelet method to estimate a sharp change point and a nonparametric regression function under random design, whose noise is heavy tailed infinite-varianced process. By using two-step method, we propose a truncation estimator of the change point, which can weaken the influence of outliers. Moreover, the convergence rate is established. Finally we obtain a wavelet estimator of the regression function. The results of numerical simulation as well as the IBM stock data analysis indicate that the method is effective.
ISBN:1467325813
9781467325813
ISSN:1934-1768
2161-2927