Krumscheid S, Pradas M, Pavliotis G A, Kalliadasis S
Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom.
Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom.
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Oct;92(4):042139. doi: 10.1103/PhysRevE.92.042139. Epub 2015 Oct 16.
In many physical, technological, social, and economic applications, one is commonly faced with the task of estimating statistical properties, such as mean first passage times of a temporal continuous process, from empirical data (experimental observations). Typically, however, an accurate and reliable estimation of such properties directly from the data alone is not possible as the time series is often too short, or the particular phenomenon of interest is only rarely observed. We propose here a theoretical-computational framework which provides us with a systematic and rational estimation of statistical quantities of a given temporal process, such as waiting times between subsequent bursts of activity in intermittent signals. Our framework is illustrated with applications from real-world data sets, ranging from marine biology to paleoclimatic data.
在许多物理、技术、社会和经济应用中，人们通常面临从经验数据（实验观测值）估计统计特性的任务，例如时间连续过程的平均首次通过时间。然而，通常情况下，仅直接从数据中准确可靠地估计此类特性是不可能的，因为时间序列往往太短，或者感兴趣的特定现象很少被观测到。我们在此提出一个理论计算框架，它为我们提供了对给定时间过程的统计量进行系统且合理的估计，比如间歇信号中后续活动突发之间的等待时间。我们的框架通过来自现实世界数据集的应用进行说明，范围从海洋生物学数据到古气候数据。
