Anderson-Darling test for uniformity¶
Description¶
Performs the Anderson-Darling goodness-of-fit test for the hypothesis of uniformity on the interval \([a, b]\). The test gives more weight to discrepancies in the tails than the Cramer-von Mises statistic.
Hypothesis of Uniformity The null hypothesis is that the sample comes from a uniform distribution on the interval \([a, b]\).
Test Statistic The statistic is computed from the ordered sample using the log cumulative distribution function and log survival function of the reference uniform distribution.
Usage¶
from pysatl_criterion.statistics.goodness_of_fit import (
AndersonDarlingUniformGofStatistic,
)
test_statistic = AndersonDarlingUniformGofStatistic(a=0, b=1)
statistic_result = test_statistic.execute_statistic([0.12, 0.25, 0.41, 0.53, 0.77, 0.91])
print(statistic_result)
Arguments¶
a - left boundary of the uniform distribution. Default value is 0.
b - right boundary of the uniform distribution. Default value is 1.
rvs - array-like sample data passed to execute_statistic.
Details¶
For the uniform distribution on \([a, b]\),
The implementation evaluates logcdf and logsf for ordered observations and passes them to the common Anderson-Darling statistic implementation.
Large values indicate stronger deviation from the uniform model.
Author(s)¶
Aleksandr Podmarev, Alexey Mironov
References¶
Anderson, T.W. and Darling, D.A. (1952): Asymptotic theory of certain goodness of fit criteria based on stochastic processes. - Annals of Mathematical Statistics, vol. 23, pp. 193-212.
Examples¶
from pysatl_criterion.statistics.goodness_of_fit import (
AndersonDarlingUniformGofStatistic,
)
test_statistic = AndersonDarlingUniformGofStatistic(a=0, b=1)
statistic_result = test_statistic.execute_statistic([0.12, 0.25, 0.41, 0.53, 0.77, 0.91])
print(statistic_result)