Fast computation of large scale marginal extremes with multi-dimensional covariates
| Autor: | Philip Jonathan, Kevin Ewans, David Randell, Laks Raghupathi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Computer science Applied Mathematics Computation Storm 01 natural sciences 010305 fluids & plasmas 010104 statistics & probability Computational Mathematics symbols.namesake Matrix (mathematics) Computational Theory and Mathematics 0103 physical sciences Covariate symbols Econometrics Pareto distribution 0101 mathematics Rogue wave Extreme value theory Scale (map) Physics::Atmospheric and Oceanic Physics |
| Zdroj: | Computational Statistics & Data Analysis. 95:243-258 |
| ISSN: | 0167-9473 |
| DOI: | 10.1016/j.csda.2015.09.006 |
| Popis: | Safe and reliable design and operation of fixed and floating marine structures often located in remote and hostile environments is challenging. Rigorous extreme value analysis of meteorological and oceanographic data can greatly aid the design of such structures. Extreme value analysis is typically undertaken for single spatial locations or for small neighbourhoods; moreover, non-stationary effects of covariates on extreme values are typically accommodated in an ad-hoc manner. The objective of the work summarised here is to improve design practice by estimating environmental design conditions (such as return values for extreme waves, winds and currents) for a whole ocean basin, including additional covariate effects (such as storm direction) as necessary, in a consistent manner. Whole-basin non-stationary extreme value modelling is computationally complex, requiring inter-alia the estimation of tail functions, the parameters of which vary with respect to multi-dimensional covariates characterised by us using tensor products of penalised B-splines. We outline two technical contributions which make whole-basin non-stationary analysis feasible. Firstly, we adopt generalised linear array methods to reduce the computational burden of matrix manipulations. Secondly, using high-performance computing, we develop a parallel implementation of maximum likelihood estimation for the generalised Pareto distribution. Together, these innovations allow estimation of rigorous whole-basin extreme value models in reasonable time. We evaluate the new approach in application to marginal extreme value modelling of storm peak significant wave heights in two ocean basins, accommodating spatial and directional covariate effects. |
| Databáze: | OpenAIRE |
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