R package texmex: Statistical modelling of extreme values. Statistical extreme value modelling of threshold excesses, maxima and multivariate extremes. Univariate models for threshold excesses and maxima are the Generalised Pareto, and Generalised Extreme Value model respectively. These models may be fitted by using maximum (optionally penalised-)likelihood, or Bayesian estimation, and both classes of models may be fitted with covariates in any/all model parameters. Model diagnostics support the fitting process. Graphical output for visualising fitted models and return level estimates is provided. For serially dependent sequences, the intervals declustering algorithm of Ferro and Segers is provided, with diagnostic support to aid selection of threshold and declustering horizon. Multivariate modelling is performed via the conditional approach of Heffernan and Tawn, with graphical tools for threshold selection and to diagnose estimation convergence.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Janßen, Anja; Wan, Phyllis: (k)-means clustering of extremes (2020)
- Leonelli, Manuele; Gamerman, Dani: Semiparametric bivariate modelling with flexible extremal dependence (2020)
- Bader, Brian; Yan, Jun; Zhang, Xuebin: Automated threshold selection for extreme value analysis via ordered goodness-of-fit tests with adjustment for false discovery rate (2018)
- Brian Bader: Automated, Efficient, and Practical Extreme Value Analysis with Environmental Applications (2016) arXiv
- Eric Gilleland and Richard Katz: extRemes 2.0: An Extreme Value Analysis Package in R (2016) not zbMATH
- Gilleland, Eric; Ribatet, Mathieu; Stephenson, Alec G.: A software review for extreme value analysis (2013)
- Nadarajah, Saralees; Afuecheta, Emmanuel; Chan, Stephen: A double generalized Pareto distribution (2013)
- Papastathopoulos, Ioannis; Tawn, Jonathan A.: Extended generalised Pareto models for tail estimation (2013)