Conditioning on an extreme component: Model consistency with regular variation on cones
By Bikramjit Das, Sidney I. Resnick. 2008
71 citations recorded by the source corpus.
Multivariate extreme value theory assumes a multivariate domain of attraction condition for the distribution of a random vector necessitating that each component satisfies a marginal domain of attraction condition. [12] and [11] developed an approximation to the joint distribution of the random vector by conditioning that one of the components be extreme. Prior papers left unresolved the consistency of dierent models obtained by conditioning on dierent components being extreme and we provide understanding of this issue. We also clarify the relationship between these conditional distributions, multivariate extreme value theory, and standard regular variation on cones of the form [0,1] ◊ (0,1].
Subfields: Finance, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/28adc50f1d96c9671253417b5c0a9a0a51945767
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Finance
- Raphaël Huser
- Emeric Thibaud
- A. C. Davison
- Sebastian Engelke
- Thomas Opitz
- Marc G. Genton
- Zakhar Kabluchko
- Mathieu Ribatet
In Management Science and Operations Research
- Raphaël Huser
- Philippe Soulier
- Rafal Kulik
- Zakhar Kabluchko
- A. C. Davison
- Sebastian Engelke
- Emeric Thibaud
- Martin Schlather
In Statistics and Probability
- Raphaël Huser
- Sebastian Engelke
- A. C. Davison
- Martin Schlather
- Emeric Thibaud
- Philippe Soulier
- Rafal Kulik
- Zakhar Kabluchko
Citations in the corpus
- Estimation of Hüsler–Reiss distributions and Brown–Resnick processes (2012, background): “…approach generalizes classical multivariate regular variation and was introduced by Heffernan and Tawn (2004) and further investigated by several researchers (see Hashorva (2006), Balkema and Embrechts (2007), Heffernan and Resnick (2007), Fougères and Soulier (2010) and Das and Resnick (2011)).”
- Heavy tailed time series with extremal independence (2013, methodology): “Conversely, it is conjectured but not proved in [DR11] that extremal independence and existence of a conditional limit law implies HRV.”
- Geostatistics of Dependent and Asymptotically Independent Extremes (2013, background): “…its introduction by Heffernan and Tawn (2004), this approach has been developed from the theoretical viewpoint by Heffernan and Resnick (2007), Das and Resnick (2011), and Fougères and Soulier (2012), among others, and has been applied to spatial flood estimation by Keef et al. (2009, 2013).”