Anderson-Darling GOF test.
function AndersonDarling(const Data: TVec; Distribution: TDistribution; out hRes: THypothesisResult; out Signif: double; Alpha: double = 0.05): double;
Parameters |
Description |
Data |
Stores ordered data. |
Distribution |
Perform test for this distribution. Supported distributions : exponential, log-normal, normal and weibull. |
hRes |
Returns the result of the null hypothesis. |
Signif |
(Significance level) returns the probability of observing the given result by chance given that the null hypothesis is true. |
Alpha |
Defines the desired significance level. If the significance probability (Signif) is bellow the desired significance (Alpha), the null hypothesis is rejected. |
Anderson-Darling test statistics, adjusted with small sample size factor.
The Anderson-Darling test (Stephens, 1974) is used to test if a sample of data came from a population with a specific distribution. It is a modification of the Kolmogorov-Smirnov (K-S) test and gives more weight to the tails than does the K-S test. The K-S test is distribution free in the sense that the critical values do not depend on the specific distribution being tested. The Anderson-Darling test makes use of the specific distribution in calculating critical values. This has the advantage of allowing a more sensitive test and the disadvantage that critical values must be calculated for each distribution.
The Anderson-Darling test is defined as:
The test statistics is defined as:
where F is the cumulative distribution function of distribution being tested.
To learn more about A-D test, check the following links:
Note
The basic assumption is that the data values are sorted in ascending order.
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