Bayesian modelling of marked point processes with incomplete records: volcanic eruptions

Ting Wang, Matthew Schofield, Mark Bebbington and Koji Kiyosugi

Journal of the Royal Statistical Society Series C, 2020, vol. 69, issue 1, 109-130

Abstract: Modelling point processes with incomplete records is a challenging problem, especially when the degree of record completeness varies over time. For volcanic eruption records, we expect the degree of missingness to depend on both the time and the size of an eruption. We propose a time‐varying intensity function for a marked point process to model the non‐stationary variation of the observed point process caused by missing data. We apply this model to global and regional volcanic eruption records and use Bayesian inference to obtain hazard estimates and their uncertainties based on the observed incomplete records, to carry out residual analysis and to provide forecasts.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/rssc.12380

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:69:y:2020:i:1:p:109-130

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().