Goodness-of-fit tests for type-I extreme-value and 2-parameter Weibull distributions

1999 ◽  
Vol 48 (1) ◽  
pp. 79-86 ◽  
Author(s):  
T. Shimokawa ◽  
Min Liao
Keyword(s):  
Goodness Of Fit ◽  
Extreme Value ◽  
Type I ◽  
Weibull Distributions ◽  
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.

Goodness-of-fit tests and applications for left-truncated Weibull distributions to non-life insurance

2015 ◽  
Vol 5 (1) ◽  
pp. 139-163 ◽  
Author(s):  
Markus Kreer ◽  
Ayşe Kızılersü ◽  
Anthony W. Thomas ◽  
Alfredo D. Egídio dos Reis
Keyword(s):  
Life Insurance ◽  
Goodness Of Fit ◽  
Weibull Distributions ◽  
Goodness Of Fit Tests

scholarly journals Genotypic diversity: estimation and prediction in samples.

Genetics ◽  
1988 ◽  
Vol 118 (4) ◽  
pp. 705-711 ◽  
Author(s):  
J A Stoddart ◽  
J F Taylor
Keyword(s):  
Goodness Of Fit ◽  
Type I Error ◽  
Genotypic Diversity ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type I Error Rates ◽  
Goodness Of Fit Tests ◽  
High Level ◽  
Better Than

Abstract We show that a commonly used statistic of genotypic diversity can be used to reflect one form of deviation from panmixia, viz. clonal reproduction, by comparing observed and predicted sample statistics. The characteristics of the statistic, in particular its relationship with population genotypic diversity, are formalised and a method of predicting the genotypic diversity of a sample drawn from a panmictic population using allelic frequencies and sample size is developed. The sensitivity of some possible tests of significance of the deviation from panmictic expectations is examined using computer simulations. Goodness-of-fit tests are robust but produce an unacceptably high level of type II error. With means and variances calculated either from Monte Carlo simulations or from distributional and series approximations, t-tests perform better than goodness-of-fit tests. Under simulation, both forms of t-test exhibit acceptable rates of type I error. Rates of type II are usually large when allele frequencies are severely skewed although the latter test performs the better in those conditions.

Sign in / Sign up

Export Citation Format

Share Document