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

Zhongnan Jin1, Lu Lu2, Khaled Bedair3,4

  • 1Department of Statistics, Virginia Tech, Blacksburg, VA, USA.

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|June 27, 2022
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Summary

Researchers developed a new statistical model to understand Yellowstone geyser eruption patterns. This covariate-adjusted recurrent event model analyzes interdependent geyser systems, improving our understanding of eruption timing and influencing factors.

Keywords:
Competing risksYellowstone National Parkcopulaevent dependencegap timerecurrent events

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Area of Science:

  • Geosciences
  • Statistical Modeling
  • Ecology

Background:

  • Geyser eruptions, particularly at Yellowstone National Park, are complex phenomena.
  • Understanding the factors influencing eruption timing and the interdependence between geysers is crucial for scientific study.

Purpose of the Study:

  • To propose a novel parametric covariate-adjusted recurrent event model.
  • To analyze the interdependence of geyser eruptions and the impact of covariates on eruption gap time.

Main Methods:

  • Development of a bivariate recurrent event process using a bivariate lognormal distribution and Gumbel copula.
  • Application of maximum likelihood estimation for parameter estimation.
  • Analysis of Yellowstone geyser eruption data for a bivariate geyser system.

Main Results:

  • The proposed model effectively estimates eruption gap times in interdependent geyser systems.
  • The analysis provides deeper insights into the mechanisms driving individual geyser events and the system as a whole.
  • Simulation studies confirm the performance of the proposed method.

Conclusions:

  • The covariate-adjusted recurrent event model offers a robust framework for studying complex event systems like geysers.
  • This research enhances the understanding of geyser eruption dynamics and the influence of external factors.
  • The findings contribute to the broader field of statistical analysis for natural phenomena.