Modeling bivariate geyser eruption system with covariate-adjusted recurrent event process
Modeling bivariate geyser eruption system with covariate-adjusted recurrent event process
复制标题
DOI：
10.1080/02664763.2021.1910937
复制
发表时间：
2021-03
影响因子：
1.5
通讯作者：
Zhongnan Jin;Lu Lu-Lu;K. Bedair;Yili Hong
中科院分区：
文献类型：
--
作者：
Zhongnan Jin;Lu Lu-Lu;K. Bedair;Yili Hong
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中的研究人员感兴趣的问题。在这篇文章中，我们提出了一个参数协变量调整的重复事件模型来估计喷发间隔时间。我们描述了一个一般的二元回归事件过程，其中二元对数正态分布和具有不同边缘分布的Gumbel Copula被用来模拟相互依赖的双型事件系统。模型参数的估计采用极大似然法。将该方法应用于一个双变量间歇泉系统的黄石间歇泉喷发数据分析，加深了对单个事件乃至整个系统事件发生机制的理解。为了评估该方法的性能，进行了全面的仿真研究。
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.