Goodness-of-fit tests for regional generalized extreme value flood distributions

1991 ◽  
Vol 27 (7) ◽  
pp. 1765-1776 ◽  
Author(s):  
Jahir Uddin Chowdhury ◽  
Jery R. Stedinger ◽  
Li-Hsiung Lu
Keyword(s):  
Goodness Of Fit ◽  
Extreme Value ◽  
Generalized Extreme Value ◽  
Goodness Of Fit Tests

scholarly journals Estimation of flood frequency using statistical method: Mahanadi River basin, India

2020 ◽  
Vol 3 (1) ◽  
pp. 189-207
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Stream Flow ◽  
Goodness Of Fit ◽  
Central India ◽  
Extreme Value ◽  
Flood Frequency ◽  
Goodness Of Fit Test ◽  
Mahanadi River ◽  
Goodness Of Fit Tests ◽  
Study Results ◽  
Anderson Darling

Abstract Estimating stream flow has a substantial financial influence, because this can be of assistance in water resources management and provides safety from scarcity of water and conceivable flood destruction. Four common statistical methods, namely, Normal, Gumbel max, Log-Pearson III (LP III), and Gen. extreme value method are employed for 10, 20, 30, 35, 40, 50, 60, 70, 75, 100, 150 years to forecast stream flow. Monthly flow data from four stations on Mahanadi River, in Eastern Central India, namely, Rampur, Sundargarh, Jondhra, and Basantpur, are used in the study. Results show that Gumbel max gives better flow discharge value than the Normal, LP III, and Gen. extreme value methods for all four gauge stations. Estimated flood values for Rampur, Sundargarh, Jondhra, and Basantpur stations are 372.361 m3/sec, 530.415 m3/sec, 2,133.888 m3/sec, and 3,836.22 m3/sec, respectively, considering Gumbel max. Goodness-of-fit tests for four statistical distribution techniques applied in the present study are also evaluated using Kolmogorov–Smirov, Anderson–Darling, Chi-squared tests at critical value 0.05 for the four proposed gauge stations. Goodness-of-fit test results show that Gen. extreme value gives best results at Rampur, Sundergarh, and Jondhra gauge stations followed by LP III, whereas LP III is the best fit for Basantpur, followed by Gen. extreme value.

scholarly journals Portfolio Tail Risk: A Multivariate Extreme Value Theory Approach

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1425
Author(s):  
Miloš Božović
Keyword(s):  
Extreme Value Theory ◽  
Value At Risk ◽  
Goodness Of Fit ◽  
Value Theory ◽  
Extreme Value ◽  
Theory Approach ◽  
Risk Modeling ◽  
Tail Risk ◽  
Goodness Of Fit Tests ◽  
Level Of Significance

This paper develops a method for assessing portfolio tail risk based on extreme value theory. The technique applies separate estimations of univariate series and allows for closed-form expressions for Value at Risk and Expected Shortfall. Its forecasting ability is tested on a portfolio of U.S. stocks. The in-sample goodness-of-fit tests indicate that the proposed approach is better suited for portfolio risk modeling under extreme market movements than comparable multivariate parametric methods. Backtesting across multiple quantiles demonstrates that the model cannot be rejected at any reasonable level of significance, even when periods of stress are included. Numerical simulations corroborate the empirical results.

scholarly journals A bayesian analysis of the annual maximum temperature using generalized extreme value distribution

MAUSAM ◽  
2021 ◽  
Vol 72 (3) ◽  
pp. 607-618
Author(s):  
CHERAITIA HASSEN
Keyword(s):  
Goodness Of Fit ◽  
Estimation Method ◽  
Gumbel Distribution ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Maximum Temperature ◽  
Ratio Test ◽  
Annual Maximum ◽  
Generalized Extreme Value ◽  
Annual Maximum Temperature

The annual maximum temperature was modeled using the Generalized Extreme Value (GEV) distribution to Jijel weather station. The Mann-Kendall (MK) and Kwiatkowski Phillips, Schmidt and Shin (KPSS) tests suggest a stationary model without linear trend in the location parameter. The Kurtosis and the Skewness statistics indicated that the normality assumption was rejected. The Likelihood Ratio test was used to determine the best model and the goodness-of-fit tests showed that our data is suitable with a stationary Gumbel distribution. The Maximum Likelihood estimation method and the Bayesian approach using the Monte Carlo method by Markov Chains (MCMC) were used to find the parameters of the Gumbel distribution and the return levels were obtained for different periods. JEL Classification: C1, C13, C46, C490.

scholarly journals Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data

2014 ◽  
Vol 18 (3) ◽  
pp. 307-313 ◽  
Author(s):  
Gabriel C. Blain ◽  
Monica C. Meschiatti
Keyword(s):  
Serial Correlation ◽  
Goodness Of Fit ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Climate Trends ◽  
Generalized Extreme Value ◽  
Extreme Rainfall Events ◽  
Probability Of Occurrence ◽  
Auto Correlation Function ◽  
Probabilistic Structure

Soil erosion, soil saturation and floods are frequently associated with extreme rainfall events. Thus, the scientific literature agrees on the need to carry out studies that improve the assessment of the probability of occurrence of extreme rainfall values. The main goal of this study was to compare the performance of the multi-parameters distributions Wakeby, Kappa and Generalized Extreme Value in fitting the annual maximums of daily, 2-day and 3-day rainfall amounts obtained from the weather station of Campinas, located in the State of São Paulo, Brazil (1890-2012). As a secondary aim, the presence of climate trends and serial correlation in these series was also evaluated. The auto-correlation function and the Mann-Kendall tests have shown the presence of no serial correlation and climate trends in the above mentioned series. The results obtained from goodness-of-fit procedures allowed us to conclude that the Kappa and the Generalized Extreme Value distributions present the best performance in describing the probabilistic structure of the series under analysis.

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