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Strong convergence rate of estimators of change point and its application

机译:变化点估计量的强收敛速度及其应用

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摘要

Let {X-n, n >= 1} be an independent sequence with a mean shift. We consider the cumulative sum (CUSUM) estimator of a change point. It is shown that, when the rth moment of X-n is finite, for n >= 1 and r > 1, strong convergence rate of the change point estimator is r o(M(n)), for any M(n) satisfying that M(n) up arrow infinity, which has improved the results the literature. Furthermore, it is also shown that the preceding rate is still valid for some dependent or negative associate cases. We also propose an iterative algorithm to search for the location of a change point. A simulation study on a mean shift model with a stable distribution is provided, which demonstrates that the algorithm is efficient. In addition, a real data example is given for illustration.
机译:令{X-n,n> = 1}是具有均值漂移的独立序列。我们考虑一个变化点的累积和(CUSUM)估计量。结果表明,当Xn的第r个矩是有限的时,对于n> = 1且r> 1,对于任何满足的M(n),变化点估计量的强收敛率是ro(M(n)/ n)。 M(n)向上箭头无穷大,从而改善了文献结果。此外,还显示,在某些附属或否定关联案件中,先前的费率仍然有效。我们还提出了一种迭代算法来搜索更改点的位置。对具有稳定分布的均值漂移模型进行了仿真研究,表明该算法是有效的。另外,给出了真实的数据示例以用于说明。

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