TY - JOUR

T1 - Inference for extreme values under threshold-based stopping rules

AU - Barlow, Anna

AU - Sherlock, Christopher

AU - Tawn, Jonathan

PY - 2020/8/1

Y1 - 2020/8/1

N2 - There is a propensity for an extreme value analyses to be conducted as a consequence of the occurrence 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 it experiencing the UK's largest ever measured flow event in 2015. We show that without accounting for this stopping feature there would be substantial over-design in response to the event.

AB - There is a propensity for an extreme value analyses to be conducted as a consequence of the occurrence 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 it experiencing the UK's largest ever measured flow event in 2015. We show that without accounting for this stopping feature there would be substantial over-design in response to the event.

U2 - 10.1111/rssc.12420

DO - 10.1111/rssc.12420

M3 - Journal article

VL - 69

SP - 765

EP - 789

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

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

SN - 0035-9254

IS - 4

ER -