ABSTRACT

Geyser eruption is one of the most popular signature attractions at the Yellowstone National Park. The interdependence of geyser eruptions and impacts of covariates are of interest to researchers in geyser studies. In this paper, we propose a parametric covariate-adjusted recurrent event model for estimating the eruption gap time. We describe a general bivariate recurrent event process, where a bivariate lognormal distribution and a Gumbel copula with different marginal distributions are used to model an interdependent dual-type event system. The maximum likelihood approach is used to estimate model parameters. The proposed method is applied to analyzing the Yellowstone geyser eruption data for a bivariate geyser system and offers a deeper understanding of the event occurrence mechanism of individual events as well as the system as a whole. A comprehensive simulation study is conducted to evaluate the performance of the proposed method.

摘要

间歇泉喷发是黄石国家公园最受欢迎的标志性景点之一。间歇泉喷发的相互依赖性和协变量的影响是间歇泉研究人员感兴趣的。在本文中，我们提出了一种参数协变量调整的复发事件模型，用于估计喷发间隙时间。我们描述了一个通用的双变量循环事件过程，其中使用双变量对数正态分布和具有不同边际分布的 Gumbel copula 来模拟相互依赖的双类型事件系统。最大似然法用于估计模型参数。所提出的方法应用于分析双变量间歇泉系统的黄石间歇泉喷发数据，可以更深入地了解单个事件以及整个系统的事件发生机制。