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Parameter estimation from observations of first-passage times of the Ornstein-Uhlenbeck process and the Feller process

机译:从奥恩斯坦 - uhlenbeck过程的第一段通过时间和FELLER过程的观察结果估计

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Renewal point processes show up in many different fields of science and engineering. In some cases the renewal points become the only observable parts of an anticipated hidden random variation of some physical quantity. The hypothesis might be that a hidden random process originating from zero or some other low value only becomes visible at the time of first crossing of some given value level, and that the process is restarted from scratch immediately after the level crossing. It might then be of interest to reveal the defining properties of this hidden process from a sample of observed first-passage times. In this paper the hidden process is first anticipated as a non-stationary Ornstein-Uhlenbeck (OU) process with unknown parameters that have to be estimated only by use of the information contained in a sample of first-passage times. The estimation method is a direct application of the Chapman-Kolmogorov integral equation of the OU process. A non-stationary Feller process is considered subsequently. As the OU process the Feller process has a known transition probability distribution that allows the formulation of the Chapman-Kolmogorov integral equation. The described integral equation estimation method also provides a subjective graphical test of the applicability of the OU process or the Feller process when applied to a reasonably large sample of observed first-passage data. These non-stationary processes have several applications in biomedical research, for example as idealized models of the neuron membrane potential. When the potential reaches a certain threshold the neuron fires, whereupon the potential drops to a fixed initial value, from where it continuously builds up again until next firing, etc. Also in civil engineering there are hidden random phenomena such as internal cracking or corrosion that after some random time break through to the material surface and become observable. However, the OU process has as a model of physical phenomena the defect of not being bounded to the negative side. This defect is not present for the Feller process, which therefore may provide a useful modeling alternative to the OU process.
机译:更新点流程显示在许多不同的科学和工程领域。在某些情况下,更新点成为预期隐藏的随机变化的唯一可观察部分。假设可能是源自零或其他低值的隐藏随机过程仅在第一交叉的时间在一些给定值水平时可见,并且该过程在级别交叉后立即从划痕重新启动。然后,它可能有兴趣地揭示从观察到的首发时间的样本的隐藏过程的定义属性。在本文中,首先将隐藏的过程作为非静止的Ornstein-Uhlenbeck(OU)过程,其具有未知参数,这些过程必须仅通过使用第一通道时间样本中包含的信息来估计。估计方法是直接应用OU过程的Chapman-Kolmogorov积分方程。随后考虑了非静止的FELLER过程。随着OU过程,Feller Process具有已知的过渡概率分布,允许制定Chapman-Kolmogorov积分方程。所描述的整体式估计方法还提供了在应用于相当大的观察到的第一通道数据样本时的OU过程的适用性的主观图形测试。这些非静止方法在生物医学研究中具有若干应用,例如作为神经元膜势的理想化模型。当潜力达到某个阈值时,神经元火灾,在此潜在的爆发到固定的初始值,从它持续再次建立之前,直到地下射击等也存在隐藏的随机现象,如内部开裂或腐蚀在一些随机时间突破材料表面并变得可观察到。然而,OU过程作为物理现象的模型,没有被束缚在负面的缺陷。对于FELLER过程,该缺陷不存在,因此可以为OU过程提供有用的建模替代方案。

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