Bayesian analysis of extreme events with threshold estimation

By Cibele N Behrens, Hedibert Freitas Lopes, Dani Gamerman. 2004

236 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/555dc020d0e66458162e394a1a5d11d5a8a3b640

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bruno Sansó
2. Carl John Scarrott
3. Jonathan A. Tawn
4. Y. Qiu
5. Miguel A. Losada
6. S. Solari
7. Jennifer L. Wadsworth
8. Matt Taddy

In Finance

1. Jennifer L. Wadsworth
2. Gabriel Huerta
3. Bruno Sansó
4. Carl John Scarrott
5. Jonathan A. Tawn
6. Y. Qiu
7. Miguel A. Losada
8. S. Solari

In Statistics and Probability

1. Miguel A. Losada
2. S. Solari
3. Gabriel Huerta
4. Bruno Sansó
5. Carl John Scarrott
6. Jonathan A. Tawn
7. Y. Qiu
8. Jennifer L. Wadsworth

Citations in the corpus

• ESTIMATION OF TAIL RISK MEASURES IN FINANCE: APPROACHES TO EXTREME VALUE MIXTURE MODELING (2019, methodology): “…Normal with GPD for upper tail (Cabras and Castellanos 2011) • Gamma with GPD for upper tail (Behrens et al. 2004) • Weibull with GPD for upper tail (Behrens et al. 2004) • Log-normal with GPD for upper tail (Solari and Losada 2004) • Beta with GPD for upper tail (MacDonald 2012) • Normal…”
• A unified statistical model for hydrological variables including the selection of threshold for the peak over threshold method (2012, methodology): “In line with Vaz de Melo Mendes and Freitas Lopes [2004], Behrens et al. [2004], Tancredi et al. [2006], Cai et al.”
• Time-varying models for extreme values (2007, background): “This is given by H(z) = exp { − [ 1 + ξ ( z − μ σ )]−1/ξ + } (1) where μ is a location parameter, σ is a scale parameter and ξ is a shape parameter.”
• A Review of Extreme Value Threshold Estimation and Uncertainty Quantification (2012, methodology): “The multiple threshold choices show themselves as a bi- or multi-modal posteriors for the threshold, and sometimes other tail parameters; see Scarrott & MacDonald (2010), Figure 4 of Behrens et al. (2004) and Figure 2b of Tancredi et al. (2006) for example.”
• Likelihood‐based procedures for threshold diagnostics and uncertainty in extreme value modelling (2012, methodology): “Frigessi et al. (2002), Behrens et al. (2004), Mendes and Lopes (2004) and Carreau and Bengio (2009) each considered different methodologies for joining parametric forms for the body of the data to GP models for the tails.”