Modeling bivariate geyser eruption system with covariate-adjusted recurrent event process

Zhongnan Jin, Lu Lu, Khaled Bedair and Yili Hong

Journal of Applied Statistics, 2022, vol. 49, issue 10, 2488-2509

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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:49:y:2022:i:10:p:2488-2509

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DOI: 10.1080/02664763.2021.1910937

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