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| VIII.2. Tests based on the Empirical Distribution Function ("EDF tests") | ||
|---|---|---|
 | Chapter VIII. The Uncertainty modeler module |  |
This part is introducing comparison tests, sometimes called "goodness of fit" tests, which are used as test
hypothesis. This idea is to check, when considering a certain variable, whether it is following a predefined law among
the list of implemented ones: normal, lognormal and uniform ones. To do so, there are three different tests implemented
in Uranie. If one calls 
the Empirical Distribution Function of the law 
(i.e. the distribution that we'd like
to test) and 
the
reference law one wants to compare to, then, for 
the number of data in the EDF, these tests are defined as:
- Kolmogorov-Smirnov (
) [Kolmogorov33] 
- Anderson-Darling (
) [Anderson52] 
- Cramer-VonMises (
) [CramervonM62] 
In these three formulas, the 
set represents the ordered data of the random variable 
which comes usually as the CDF distribution for convenience.
| |  | |
| Chapter VIII. The Uncertainty modeler module |  | VIII.3. The Circe method |