Zhang-Wu C test for normality

Description

Performs the Zhang-Wu C goodness-of-fit test for the hypothesis of normality. The null hypothesis is that the sample comes from a normal distribution.

Hypothesis of Normality The hypothesis of normality refers to the null hypothesis that the data comes from a normal distribution. In the implementation, the statistic is computed from the sample passed to execute_statistic.

Test Statistic The statistic is based on log-ratio transformations of fitted normal distribution values.

Usage

from pysatl_criterion.statistics.goodness_of_fit import (
    ZhangWuCNormalityGofStatistic,
)


test_statistic = ZhangWuCNormalityGofStatistic()
statistic_result = test_statistic.execute_statistic([-1.21, -0.83, -0.52, -0.31, -0.08, 0.14, 0.29, 0.47, 0.68, 0.91, 1.16, 1.43])
print(statistic_result)

Arguments

mean - reference normal mean. Default value is 0.

var - reference normal variance. Default value is 1.

rvs - array-like sample data passed to execute_statistic.

Details

The implementation evaluates the Zhang-Wu C statistic for the supplied observations. Large or small values should be interpreted according to the statistic alternative used by the class implementation.

References

The statistic follows the implementation in pysatl_criterion.statistics.goodness_of_fit.normal.

Author(s)

Alexey Mironov

Examples

from pysatl_criterion.statistics.goodness_of_fit import (
    ZhangWuCNormalityGofStatistic,
)


test_statistic = ZhangWuCNormalityGofStatistic()
statistic_result = test_statistic.execute_statistic([-1.21, -0.83, -0.52, -0.31, -0.08, 0.14, 0.29, 0.47, 0.68, 0.91, 1.16, 1.43])
print(statistic_result)