Assessment of return value estimates from stationary and non-stationary extreme value models
Mackay, E; Jonathan, P
Date: 27 April 2020
Journal
Ocean Engineering
Publisher
Elsevier
Publisher DOI
Related links
Abstract
This article compares the accuracy of return value estimates from stationary and non-stationary extreme value models
when the data exhibits covariate dependence. The non-stationary covariate representation used is a penalised piecewiseconstant (PPC) model, in which the data are partitioned into bins defined by covariates and the extreme ...
This article compares the accuracy of return value estimates from stationary and non-stationary extreme value models
when the data exhibits covariate dependence. The non-stationary covariate representation used is a penalised piecewiseconstant (PPC) model, in which the data are partitioned into bins defined by covariates and the extreme value distribution
is assumed to be homogeneous within each bin. A generalised Pareto model is assumed, where the scale parameter can
vary between bins but is penalised for the variance across bins, and the shape parameter is assumed constant over all
covariate bins. The number and sizes of covariate bins must be defined by the user based on physical considerations.
Numerical simulations are conducted to compare the performance of stationary and non-stationary models for various
case studies, in terms of quality of estimation of the T-year return value over the full covariate domain. It is shown that a
non-stationary model can give improved estimates of return values, provided that model assumptions are consistent with
the data. When the data exhibits non-stationarity in the generalised Pareto tail shape, the use of non-stationary model
assuming a constant shape parameter can produce biases in return values. In such cases, a stationary model can give a
more accurate estimate of return value over the full covariate domain as only the most extreme observations (regardless of
covariate) are used to estimate tail shape. In other cases, the assumption of a stationary model will ignore key features of
the data and be less reliable than a non-stationary model. For example, if a relatively benign covariate interval exhibits
a long (or heavy) tail, extreme values from this interval may influence the T-year return value for very large T. However
the sample of peaks over threshold, with high threshold, used to estimate a stationary model in this case may not include
sufficient observations from this interval to estimate the return value adequately.
Engineering
Faculty of Environment, Science and Economy
Item views 0
Full item downloads 0
Except where otherwise noted, this item's licence is described as © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)