A folding methodology for multivariate extremes: estimation of the spectral probability measure and actuarial applications
DOI10.1080/03461238.2013.864326zbMath1401.62209OpenAlexW2030019168WikidataQ116752801 ScholiaQ116752801MaRDI QIDQ4576914
Alexandre You, Philippe Naveau, Armelle Guillou
Publication date: 11 July 2018
Published in: Scandinavian Actuarial Journal (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03461238.2013.864326
point processesextreme value theoryfoldingmultivariate regular variationreinsurance pricingspectral probability measure
Applications of statistics to actuarial sciences and financial mathematics (62P05) Inference from stochastic processes and spectral analysis (62M15) Extreme value theory; extremal stochastic processes (60G70) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)
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Cites Work
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- Estimating a multidimensional extreme-value distribution
- Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribu\-tion
- Estimation of the extreme-value index and generalized quantile plots
- A moment estimator for the index of an extreme-value distribution
- Improving extreme quantile estimation via a folding procedure
- Statistical inference using extreme order statistics
- A simple general approach to inference about the tail of a distribution
- Estimating the spectral measure of an extreme value distribution
- Sea and wind: multivariate extremes at work
- Excess functions and estimation of the extreme-value index
- The sample autocorrelations of heavy-tailed processes with applications to ARCH
- Nonparametric estimation of the spectral measure of an extreme value distribution.
- SHIFT AND SCALE COUPLING METHODS FOR PERFECT SIMULATION
- Bivariate extreme value theory: Models and estimation
- Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
- Limit theory for multivariate sample extremes
- Estimation of Parameters and Larger Quantiles Based on the k Largest Observations
- Statistics of Extremes
- Heavy-Tail Phenomena
- Understanding Relationships Using Copulas
