scholarly journals On spatial conditional extremes for ocean storm severity

2019 ◽  
Vol 30 (6) ◽  
Author(s):  
R. Shooter ◽  
E. Ross ◽  
J. Tawn ◽  
P. Jonathan
Keyword(s):  
Conditional Extremes

scholarly journals A theorem on conditional extremes with an application to total differentials

1951 ◽  
Vol 2 (3) ◽  
pp. 423-423
Author(s):  
L. M. Court
Keyword(s):  
Conditional Extremes

scholarly journals Conditional Extremes in Asymmetric Financial Markets

2018 ◽  
Vol 38 (1) ◽  
pp. 201-213 ◽  
Author(s):  
Natalia Nolde ◽  
Jinyuan Zhang
Keyword(s):  
Financial Markets ◽  
Conditional Extremes

scholarly journals A note on the asymptotic behavior of conditional extremes

1995 ◽  
Vol 25 (2) ◽  
pp. 163-170 ◽  
Author(s):  
Ashis K. Gangopadhyay
Keyword(s):  
Asymptotic Behavior ◽  
Conditional Extremes

scholarly journals Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP

2021 ◽  
pp. 1-1
Author(s):  
Jethro Browell ◽  
Matteo Fasiolo
Keyword(s):  
Probabilistic Forecasting ◽  
Conditional Extremes ◽  
Net Load

scholarly journals On the Estimation of Ocean Engineering Design Contours

2011 ◽  
Author(s):  
Philip Jonathan ◽  
Kevin Ewans ◽  
Jan Flynn
Keyword(s):  
Joint Estimation ◽  
New Method ◽  
Ocean Engineering ◽  
Bootstrap Analysis ◽  
North West ◽  
The North ◽  
Reliability Method ◽  
Conditional Extremes ◽  
Structural Loading ◽  
Joint Behaviour

Understanding extreme ocean environments and their interaction with fixed and floating structures is critical for offshore and coastal design. Design contours are useful to describe the joint behaviour of environmental, structural loading and response variables. We compare different forms of design contours, using theory and simulation, and present a new method for joint estimation of contours of constant exceedence probability for a general set of variables. The method is based on a conditional extremes model from the statistics literature, motivated by asymptotic considerations. We simulate under the conditional extremes model to estimate contours of constant exceedence probability. We also use the estimated conditional extremes model to estimate other forms of design contours, including those based on the First Order Reliability Method, without needing to specify the functional forms of conditional dependence between variables. We demonstrate the application of new method in estimation of contours of constant exceedence probability using measured and hindcast data from the Northern North Sea, the Gulf of Mexico and the North West Shelf of Australia, and quantify their uncertainties using a bootstrap analysis.

Non-stationary conditional extremes of northern North Sea storm characteristics

2014 ◽  
Vol 25 (3) ◽  
pp. 172-188 ◽  
Author(s):  
P. Jonathan ◽  
K. Ewans ◽  
D. Randell
Keyword(s):  
North Sea ◽  
Conditional Extremes ◽  
Northern North Sea ◽  
Storm Characteristics ◽  
Sea Storm

Evaluating environmental joint extremes for the offshore industry using the conditional extremes model

2014 ◽  
Vol 130 ◽  
pp. 124-130 ◽  
Author(s):  
Kevin Ewans ◽  
Philip Jonathan
Keyword(s):  
Conditional Extremes ◽  
Offshore Industry

Spatial conditional extremes via the Gibbs sampler.

2020 ◽  
Author(s):  
Adrian Casey ◽  
Ioannis Papastathopoulos
Keyword(s):  
Gibbs Sampler ◽  
Goodness Of Fit ◽  
Value Theory ◽  
New Method ◽  
Main Element ◽  
Asymptotic Independence ◽  
Random Component ◽  
Data Set ◽  
Spatial Extremes ◽  
Conditional Extremes

<div> <div> <div> <p>Spatial conditional extremes via the Gibbs sampler.</p> <p>Adrian Casey, University of Edinburgh</p> <p>January 14, 2020</p> <p>Conditional extreme value theory has been successfully applied to spatial extremes problems. In this statistical method, data from observation sites are modelled as appropriate asymptotic characterisations of random vectors <strong>X,</strong> conditioned on one of their components being extreme. The method is generic and applies to a broad range of dependence structures including asymptotic dependence and asymptotic independence. However, one issue that affects the conditional extremes method is the necessity to model and fit a multi-dimensional residual distribution; this can be challenging in spatial problems with a large number of sites.</p> <p>We describe early-stage work that takes a local approach to spatial extremes; this approach explores lower dimensional structures that are based on asymptotic representations of Markov random fields. The main element of this new method is a model for the behaviour of a random component X<sub>i </sub> given that its nearest neighbours exceed a sufficiently large threshold. When combined with a model for the case where the neighbours are below this threshold, a Gibbs sampling scheme serves to induce a model for the full conditional extremes distribution by taking repeated samples from these local (univariate) distributions.</p> <p>The new method is demonstrated on a data set of significant wave heights from the North Sea basin. Markov chain Monte-Carlo diagnostics and goodness-of-fit tests illustrate the performance of the method. The potential for extrapolation into the outer reaches of the conditional extreme tails is then examined.</p> <p>Joint work with Ioannis Papastathopoulos.</p> </div> </div> </div>

scholarly journals Conditional Extremes and Near-Extremes

2001 ◽  
Author(s):  
Victor Chernozhukov
Keyword(s):  
Conditional Extremes
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