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Statistical power estimation dataset for external validation GoF tests on EVT distribution
Federico Reghenzani1, Giuseppe Massari1, Luca Santinelli2
1DEIB, Politecnico di Milano, Milano, Italy.
Data in Brief
|June 19, 2019
Summary
This study estimates the statistical power of goodness-of-fit tests for Extreme Value Theory (EVT) distributions. The findings aid in selecting appropriate sample sizes for external validation using quantitative power data.
Area of Science:
- Statistics
- Extreme Value Theory (EVT)
Background:
- Goodness-of-fit tests are crucial for validating Extreme Value Theory (EVT) models.
- Determining adequate sample size is essential for reliable external validation of EVT distributions.
Purpose of the Study:
- To provide precise statistical power estimations for goodness-of-fit tests applied to EVT distributions.
- To generate a dataset enabling informed sample size selection in external validation contexts.
Main Methods:
- Utilized a Monte Carlo simulation approach for high-precision power estimations.
- Calculated statistical power for Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Maximum Absolute Deviation (MAD) tests.
Main Results:
- Quantitative data on the statistical power of KS, AD, and MAD tests for EVT distributions were computed.
- The raw dataset from these power estimations is publicly available for future research.
Conclusions:
- The study offers essential data for researchers validating EVT models.
- The generated dataset serves as a valuable resource for sample size determination in statistical modeling.
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