Minimum Sample Size Determination for Generalized Extreme Value Distribution
Communications in Statistics - Simulation and Computation, 2010 Sample size determination is an important issue in statistical analysis. Obviously, the larger the sample size is, the better the statistical results we have. However, in many areas such as coastal engineering and environmental sciences, it can be very expensive or even impossible to collect large samples. In this paper, we propose a general method for
Cai, Yuzhi, Hames, Dominic
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Statistical Modelling of Extreme Data of Air Pollution in Pekanbaru City
InPrime, 2019 Air pollution is a phenomenon that is often discussed, especially regarding air quality in urban areas. This has become a major contributor to health problems and environmental issues in Asian countries, such as Indonesia, especially Riau Province.
Ari Pani Desvina +2 more
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Spatial Patterns in the Extreme Dependence of Ozone Pollution between Cities in China’s BTH Region
Atmosphere, 2023 Ozone pollution in China has become increasingly severe in recent years. Considering the damage that extreme ozone pollution may cause and the fact that the occurrence of extreme ozone pollution among different locations may be related, this paper uses ...
Lu Deng, Siqi Sheng
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Evaluation of the best fit distribution for partial duration series of daily rainfall in Madinah, western Saudi Arabia [PDF]
Proceedings of the International Association of Hydrological Sciences, 2014 Rainfall frequency analysis is an essential tool for the design of water related infrastructure. It can be used to predict future flood magnitudes for a given magnitude and frequency of extreme rainfall events.
F. Alahmadi +4 more
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Bayesian modeling of dynamic extreme values: extension of generalized extreme value distributions with latent stochastic processes [PDF]
Journal of Applied Statistics, 2016 ABSTRACTThis paper develops Bayesian inference of extreme value models with a flexible time-dependent latent structure. The generalized extreme value distribution is utilized to incorporate state variables that follow an autoregressive moving average (ARMA) process with Gumbel-distributed innovations.
Jouchi Nakajima +2 more
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Cramer-von Mises and Anderson-Darling goodness of fit tests for extreme value distributions with unknown parameters [PDF]
, 2004 The use of goodness of fit tests based on Cramer-von Mises and Anderson-Darling statistics is discussed, with reference to the composite hypothesis that a sample of observations comes from a distribution, FH, whose parameters are unspecified.
Ahmad +23 more
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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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Posterior propriety in Bayesian extreme value analyses using reference priors [PDF]
, 2015 The Generalized Pareto (GP) and Generalized extreme value (GEV) distributions play an important role in extreme value analyses, as models for threshold excesses and block maxima respectively. For each of these distributions we consider Bayesian inference
Attalides, Nicolas, Northrop, Paul J.
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Flood Frequency Analyses over Different Basin Scales in the Blue Nile River Basin, Ethiopia
Hydrology, 2020 The frequency and intensity of flood quantiles and its attendant damage in agricultural establishments have generated a lot of issues in Ethiopia. Moreover, precise estimates of flood quantiles are needed for efficient design of hydraulic structures ...
Getachew Tegegne +3 more
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Extreme Value Analysis of Statistically Independent Stochastic Variables [PDF]
한국해양공학회지, 2019 An extreme value analysis (EVA) is essential to obtain a design value for highly nonlinear variables such as long-term environmental data for wind and waves, and slamming or sloshing impact pressures.
Yongho Choi +3 more
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