Truncating Estimation for the Mean Change-Point in Heavy-Tailed Dependent Observations

Abstract

This article considers the problem of a mean change-point in heavy-tailed dependent observations. A method of change-point estimation by truncating initial process is proposed, which can weaken the affection of outliers. In the infinite variance case, we obtained a generalization Hájek-Rényi type inequality. Consistency and the rate of convergence for the estimated change-point are also established. The results of a simulation study support validity of our method.

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