A Revised Bimodal Generalized Extreme Value Distribution: Theory and Climate Data Application [PDF]
EntropyThe bimodal generalized extreme value (BGEV) distribution was first introduced in 2023. This distribution offers greater flexibility than the generalized extreme value (GEV) distribution for modeling extreme and heterogeneous (bimodal) events.
Cira E. G. Otiniano +2 more
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Modeling Extreme Rainfall Using the Generalized Extreme Value Distribution and Exceedance Analysis in Colima, Mexico [PDF]
SensorsThis study develops a statistical and technological framework to analyze extreme rainfall in Colima, Mexico, by integrating historical precipitation records, probabilistic modeling, and spatial visualization.
Raúl Renteria +2 more
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Bivariate q- generalized extreme value distribution: A comparative approach with applications to climate related data [PDF]
HeliyonThe premise of extreme value theory focuses on the stochastic behaviour and occurrence of extreme observations in an event that is random. Traditionally for univariate case, the behaviour of the maxima is described either by the types-I, types-II or ...
Laila A. Al-Essa +5 more
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Prediction of extreme rainfall with a generalized extreme value distribution [PDF]
Journal of the Korean Data and Information Science Society, 2013 Extreme rainfall causes heavy losses in human life and properties. Hence many works have been done to predict extreme rainfall by using extreme value distributions. In this study, we use a generalized extreme value distribution to derive the posterior predictive density with hierarchical Bayesian approach based on the data of Seoul area from 1973 to ...
Yong Kyu Sung, Joong K. Sohn
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On the maximum likelihood estimator for the Generalized Extreme-Value distribution [PDF]
Extremes, 2017 The vanilla method in univariate extreme-value theory consists of fitting the three-parameter Generalized Extreme-Value (GEV) distribution to a sample of block maxima. Despite claims to the contrary, the asymptotic normality of the maximum likelihood estimator has never been established.
Buecher, Axel, Segers, Johan
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GENERALIZED EXTREME VALUE DISTRIBUTION PARAMETERS AS DYNAMICAL INDICATORS OF STABILITY [PDF]
International Journal of Bifurcation and Chaos, 2012 We introduce a new dynamical indicator of stability based on the Extreme Value statistics showing that it provides an insight into the local stability properties of dynamical systems. The indicator performs faster than others based on the iteration of the tangent map since it requires only the evolution of the original systems and, in the chaotic ...
Davide Faranda +3 more
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Reliability of Extreme Wind Speeds Predicted by Extreme-Value Analysis
Meteorology, 2023 The reliability of extreme wind speed predictions at large mean recurrence intervals (MRI) is assessed by bootstrapping samples from representative known distributions.
Nicholas John Cook
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Bootstrapping Time-Varying Uncertainty Intervals for Extreme Daily Return Periods
International Journal of Financial Studies, 2022 This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data.
Katleho Makatjane, Tshepiso Tsoku
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The Topp-Leone generalized extreme value distribution: Extreme value analysis and return level estimation of the PM2.5 in Chiang Mai, Thailand [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2022 In this paper, an extension of the generalized extreme value (GEV) distribution called the Topp Leone-GEV (TL-GEV) distribution is applied. The TL-GEV distribution has four parameters (λ, μ, σ, ξ), and it has the three named sub-models TLGumbel (for ξ =
Sirinapa Aryuyuen, Winai Bodhisuwan
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A mixture transmuted generalized extreme value distribution: Definition and properties [PDF]
EPJ Web of ConferencesExtreme events are often described using generalized extreme value models, which are crucial for quantifying their impact. In prior studies, researchers have utilized the quadratic rank transmutation map to construct a comprehensive family of probability
Yang Yu, Sun HongGuang, Xu Zheng
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