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

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.

Keywords:

• Competing risks
• copula
• event dependence
• gap time
• recurrent events
• Yellowstone National Park

Acknowledgements

The authors thank the editor, associate editor, and two referees, for their valuable comments that helped in improving the paper significantly. The authors acknowledge the Advanced Research Computing program at Virginia Tech for providing computational resources. The work by Hong was partially supported by National Science Foundation Grant CMMI-1904165 to Virginia Tech.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work by Hong was partially supported by National Science Foundation [grant number CMMI-1904165] to Virginia Tech.