Kuiper test for gamma distribution¶
Description¶
Performs Kuiper goodness-of-fit test for the hypothesis that the sample comes from a gamma distribution. The statistic combines the largest positive and negative empirical distribution deviations.
Hypothesis of Gamma Distribution
The null hypothesis is that the data comes from a gamma distribution with positive shape parameter alpha and positive rate parameter beta.
Usage¶
from pysatl_criterion.statistics.goodness_of_fit import (
KuiperGammaGofStatistic,
)
test_statistic = KuiperGammaGofStatistic(alpha=2, beta=1)
statistic_result = test_statistic.execute_statistic([0.42, 0.77, 1.05, 1.48, 1.96, 2.34, 3.12])
print(statistic_result)
Arguments¶
alpha - positive shape parameter of the gamma distribution. Default value is 1.0.
beta - positive rate parameter of the gamma distribution. Default value is 1.0.
rvs - array-like sample data passed to execute_statistic.
Details¶
The implementation computes
where \(D^+\) and \(D^-\) are one-sided deviations between empirical plotting positions and gamma CDF values.
Author(s)¶
Sergey Golovachev, Alexey Mironov
References¶
Kuiper, N.H. (1960): Tests concerning random points on a circle. - Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen, Series A, vol. 63, pp. 38-47.
Examples¶
from pysatl_criterion.statistics.goodness_of_fit import (
KuiperGammaGofStatistic,
)
test_statistic = KuiperGammaGofStatistic(alpha=2, beta=1)
statistic_result = test_statistic.execute_statistic([0.42, 0.77, 1.05, 1.48, 1.96, 2.34, 3.12])
print(statistic_result)