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Patterns for the waiting time in the context of discrete-time stochastic processes

机译:离散时间随机过程的等待时间的模式

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The aim of this study is to extend the scope and applicability of the level-crossing method to discrete-time stochastic processes and generalize it to enable us to study multiple discrete-time stochastic processes. In previous versions of the level-crossing method, problems with it correspond to the fact that this method had been developed for analyzing a continuous-time process or at most a multiple continuous-time process in an individual manner. However, since all empirical processes are discrete in time, the already-established level-crossing method may not prove adequate for studying empirical processes. Beyond this, due to the fact that most empirical processes are coupled; their individual study could lead to vague results. To achieve the objectives of this study, we first find an analytical expression for the average frequency of crossing a level in a discrete-time process, giving the measure of the time experienced for two consecutive crossings named as the "waiting time." We then introduce the generalized level-crossing method by which the consideration of coupling between the components of a multiple process becomes possible. Finally, we provide an analytic solution when the components of a multiple stochastic process are independent Gaussian white noises. The comparison of the results obtained for coupled and uncoupled processes measures the strength and efficiency of the coupling, justifying our model and analysis. The advantage of the proposed method is its sensitivity to the slightest coupling and shortest correlation length.
机译:本研究的目的是将水平交叉方法的范围和适用性扩展到离散时间随机过程,并概括为使我们能够研究多个离散时间随机过程。在先前的交叉方法的版本中,它的问题对应于该方法已经开发了用于分析连续时间过程或以各个方式进行多次连续时间过程。然而,由于所有经验过程及时都是离散的,所以已经建立的水平交叉方法可能无法足以用于研究经验过程。除此之外,由于大多数经验过程耦合;他们的个人研究可能导致模糊的结果。为了实现本研究的目标,首先找到一个分析表达,用于在离散时间过程中交叉平均频率的平均频率,从而给出了两个名为“等待时间”的连续交叉路口所经历的时间的衡量标准。然后,我们介绍一般的水平交叉方法,通过该方法可以考虑多个过程的组件之间的耦合。最后,当多次随机过程的组件是独立的高斯白色噪声时,我们提供了分析解决方案。用于耦合和解耦过程获得的结果的比较测量耦合的强度和效率,证明我们的模型和分析。所提出的方法的优点是对最轻微的耦合和最短相关长度的敏感性。

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