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Long signal change-point detection

机译:长信号变化点检测

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The detection of change-points in a spatially or time-ordered data sequence is an important problem in many fields such as genetics and finance. We derive the asymptotic distribution of a statistic recently suggested for detecting change-points, thus establishing its validity. Simulation of its estimated limit distribution leads to a new and computationally efficient change-point detection algorithm, which can be used on very long signals. To finish, we briefly assess this new algorithm on one- and multi-dimensional data.
机译:在空间或时间顺序数据序列中检测变化点是许多领域的重要问题,例如遗传学和金融学。我们导出最近建议用于检测变化点的统计量的渐近分布,从而确定其有效性。仿真其估计的极限分布会导致一种新的,计算效率高的变化点检测算法,该算法可用于非常长的信号。最后,我们简要评估此新算法对一维和多维数据的影响。

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