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arXiv 0801.1598math.PRmath.STstat.TH
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分数布朗运动中有序模式概率的估计

Estimation of ordinal pattern probabilities in fractional Brownian motion

Mathieu Sinn, Karsten Keller

摘要:

对于分数布朗运动(fBm)的等距离散,d=2阶序模式的概率与Hurst参数H单调相关.通过将表示上下变化的那些模式的样本频率插入到与H的单调关系中,人们得到Hurst参数的零交叉(ZC)估计,这在数学和应用研究中得到了相当大的关注。本文主要讨论fBm中有序模式概率的估计。事实证明,根据充分性原理,对于d=2阶的有序模式,任何合理的估计量都是样本变化频率的仿射函数。我们建立了估计量的强相合性,并证明了它们在H<3/4时是渐近正态的。此外,我们推导出赫斯特参数的置信区间。仿真研究表明,ZC估计比赫斯特参数的HEAF估计具有更大的方差和更小的偏差。

英文摘要:

For equidistant discretizations of fractional Brownian motion (fBm), the probabilities of ordinal patterns of order d=2 are monotonically related to the Hurst parameter H. By plugging the sample relative frequency of those patterns indicating changes between up and down into the monotonic relation to H, one obtains the Zero Crossing (ZC) estimator of the Hurst parameter which has found considerable attention in mathematical and applied research. In this paper, we generally discuss the estimation of ordinal pattern probabilities in fBm. As it turns out, according to the sufficiency principle, for ordinal patterns of order d=2 any reasonable estimator is an affine functional of the sample relative frequency of changes. We establish strong consistency of the estimators and show them to be asymptotically normal for H<3/4. Further, we derive confidence intervals for the Hurst parameter. Simulation studies show that the ZC estimator has larger variance but less bias than the HEAF estimator of the Hurst parameter.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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