用非均质 Weibull-Hawkes 过程模拟分散不足的声学线索

IF 1.4 4区 数学 Q3 BIOLOGY
Alec B. M. Van Helsdingen, Tiago A. Marques, Charlotte M. Jones-Todd
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引用次数: 0

摘要

霍克斯点过程描述了事件到达由历史事件触发的自激行为。这些模型正日益成为分析事件类型数据的热门选择。与所有其他不均匀泊松点过程一样,霍克斯过程中事件之间的等待时间来自均值为 1 的指数分布。然而,与许多生态和环境数据一样,这是一个不切实际的假设。因此,我们对霍克斯过程进行了扩展和概括,通过假设韦布尔分布作为等待时间的基础,来考虑事件之间等待时间的潜在不足或过度分散。我们将这一模型应用于抹香鲸的声学线索产生时间,结果表明我们的 Weibull-Hawkes 模型能更好地捕捉抹香鲸发出的回声定位咔嗒声到达时间的内在低分散性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An Inhomogeneous Weibull–Hawkes Process to Model Underdispersed Acoustic Cues

An Inhomogeneous Weibull–Hawkes Process to Model Underdispersed Acoustic Cues

A Hawkes point process describes self-exciting behaviour where event arrivals are triggered by historic events. These models are increasingly becoming a popular choice in analysing event-type data. Like all other inhomogeneous Poisson point processes, the waiting time between events in a Hawkes process is derived from an exponential distribution with mean one. However, as with many ecological and environmental data, this is an unrealistic assumption. We, therefore, extend and generalise the Hawkes process to account for potential under- or overdispersion in the waiting times between events by assuming the Weibull distribution as the foundation of the waiting times. We apply this model to the acoustic cue production times of sperm whales and show that our Weibull–Hawkes model better captures the inherent underdispersion in the interarrival times of echolocation clicks emitted by these whales.

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来源期刊
CiteScore
2.70
自引率
7.10%
发文量
38
审稿时长
>12 weeks
期刊介绍: The Journal of Agricultural, Biological and Environmental Statistics (JABES) publishes papers that introduce new statistical methods to solve practical problems in the agricultural sciences, the biological sciences (including biotechnology), and the environmental sciences (including those dealing with natural resources). Papers that apply existing methods in a novel context are also encouraged. Interdisciplinary papers and papers that illustrate the application of new and important statistical methods using real data are strongly encouraged. The journal does not normally publish papers that have a primary focus on human genetics, human health, or medical statistics.
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