Work

Inference for Extreme Values Under Threshold-Based Stopping Rules

Year: 2020

Type: article

Abstract: Summary There is a propensity for an extreme value analysis to be conducted as a consequence of a large flooding event. This timing of the analysis introduces bias and poor coverage probabilities into the associated risk assessments and leads subsequently to inefficient flood protection schemes. We explore these problems through studying stochastic stopping criteria and propose new likelihood-based inferences that mitigate against these difficulties. Our methods are illustrated through the analysis of the river Lune, following its experiencing the UK's largest ever measured flow event in 2015. We show that without accounting for this stopping feature there would be substantial overdesign in response to the event.

Source: Journal of the Royal Statistical Society Series C (Applied Statistics)

DOI: 10.1111/rssc.12420

Authors: Anna Maria Barlow, Chris Sherlock, Jonathan Tawn

Institution: Lancaster University

Language: English

Topic: Statistical Distribution Estimation and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,000

Funder: Engineering and Physical Sciences Research Council