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Fast computation of large scale marginal extremes with multi-dimensional covariates

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Fast computation of large scale marginal extremes with multi-dimensional covariates. / Raghupathi, L.; Randell, D.; Ewans, K.; Jonathan, P.

In: Computational Statistics and Data Analysis, Vol. 95, 01.03.2016, p. 243-258.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Raghupathi, L, Randell, D, Ewans, K & Jonathan, P 2016, 'Fast computation of large scale marginal extremes with multi-dimensional covariates', Computational Statistics and Data Analysis, vol. 95, pp. 243-258. https://doi.org/10.1016/j.csda.2015.09.006

APA

Raghupathi, L., Randell, D., Ewans, K., & Jonathan, P. (2016). Fast computation of large scale marginal extremes with multi-dimensional covariates. Computational Statistics and Data Analysis, 95, 243-258. https://doi.org/10.1016/j.csda.2015.09.006

Vancouver

Raghupathi L, Randell D, Ewans K, Jonathan P. Fast computation of large scale marginal extremes with multi-dimensional covariates. Computational Statistics and Data Analysis. 2016 Mar 1;95:243-258. https://doi.org/10.1016/j.csda.2015.09.006

Author

Raghupathi, L. ; Randell, D. ; Ewans, K. ; Jonathan, P. / Fast computation of large scale marginal extremes with multi-dimensional covariates. In: Computational Statistics and Data Analysis. 2016 ; Vol. 95. pp. 243-258.

Bibtex

@article{ff37530b9db84e2f8c1041a7fe338155,
title = "Fast computation of large scale marginal extremes with multi-dimensional covariates",
abstract = "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. {\textcopyright} 2015 Elsevier B.V.",
keywords = "Extremes, Fast-computation, High-performance computation, Non-stationary effects, Splines, Computational efficiency, Design, Gaussian noise (electronic), Maximum likelihood, Maximum likelihood estimation, Ocean currents, Offshore structures, Pareto principle, Storms, Tensors, Value engineering, Fast computation, Generalised Pareto distributions, High performance computation, High performance computing, Meteorological and oceanographic data, Parallel implementations, Structural design",
author = "L. Raghupathi and D. Randell and K. Ewans and P. Jonathan",
year = "2016",
month = mar,
day = "1",
doi = "10.1016/j.csda.2015.09.006",
language = "English",
volume = "95",
pages = "243--258",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Fast computation of large scale marginal extremes with multi-dimensional covariates

AU - Raghupathi, L.

AU - Randell, D.

AU - Ewans, K.

AU - Jonathan, P.

PY - 2016/3/1

Y1 - 2016/3/1

N2 - 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. © 2015 Elsevier B.V.

AB - 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. © 2015 Elsevier B.V.

KW - Extremes

KW - Fast-computation

KW - High-performance computation

KW - Non-stationary effects

KW - Splines

KW - Computational efficiency

KW - Design

KW - Gaussian noise (electronic)

KW - Maximum likelihood

KW - Maximum likelihood estimation

KW - Ocean currents

KW - Offshore structures

KW - Pareto principle

KW - Storms

KW - Tensors

KW - Value engineering

KW - Fast computation

KW - Generalised Pareto distributions

KW - High performance computation

KW - High performance computing

KW - Meteorological and oceanographic data

KW - Parallel implementations

KW - Structural design

U2 - 10.1016/j.csda.2015.09.006

DO - 10.1016/j.csda.2015.09.006

M3 - Journal article

VL - 95

SP - 243

EP - 258

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

ER -