运动方向和速度的双变量变点检测
Bivariate change point detection in movement direction and speed
AI总结：
针对生物沿线性结构运动中方向和速度的突变检测问题，本文提出一种新的随机模型，用移动核估计量替代经典MOSUM统计量，并给出变点估计及区分方向与速度变点的图形化方法。
摘要：
生物运动模式有时可近似为线性，并伴有方向和速度的突变，正如本文所研究的根细胞中的质体运动。为了分析此类变化，我们提出了一种新的沿线性结构运动的随机模型。给出了最大似然估计量，并且由于增量的序列相关性，经典的MOSUM统计量被移动核估计量所替代。证明了所得差分过程的收敛性以及方差估计量的强相合性。我们估计了变点，并提出了一种图形化技术来区分运动方向变点和速度变点。
英文摘要：
Biological movement patterns can sometimes be quasi linear with abrupt changes in direction and speed, as in plastids in root cells investigated here. For the analysis of such changes we propose a new stochastic model for movement along linear structures. Maximum likelihood estimators are provided, and due to serial dependencies of increments, the classical MOSUM statistic is replaced by a moving kernel estimator. Convergence of the resulting difference process and strong consistency of the variance estimator are shown. We estimate the change points and propose a graphical technique to distinguish between change points in movement direction and speed.
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。
