Mann-Scheuer-Fertig test for Weibull distribution¶
Description¶
Performs Mann-Scheuer-Fertig goodness-of-fit test for the hypothesis that the sample comes from a Weibull distribution.
The implementation uses the Weibull distribution utilities from pysatl_criterion.core.distributions.weibull where applicable.
Hypothesis of Weibull Distribution
The null hypothesis is that the data comes from a Weibull distribution with parameters a and k.
The observations passed to execute_statistic should be positive for statistics that use logarithms or Weibull probability plots.
Test Statistic The statistic is based on a normalized-spacing statistic for right-censored-style Weibull spacing structure.
Usage¶
from pysatl_criterion.statistics.goodness_of_fit import (
MSFWeibullGofStatistic,
)
test_statistic = MSFWeibullGofStatistic(a=1, k=2)
statistic_result = test_statistic.execute_statistic([0.42, 0.65, 0.88, 1.12, 1.43, 1.76, 2.05, 2.44, 2.91, 3.37])
print(statistic_result)
Arguments¶
a - Weibull distribution parameter. Default value is 1.
k - Weibull distribution parameter. Default value is 1 for most statistics; KolmogorovSmirnovWeibullGofStatistic defaults to 5.
rvs - array-like sample data passed to execute_statistic.
Details¶
The implementation evaluates the Mann-Scheuer-Fertig statistic for the supplied observations. For EDF-based statistics, observations are transformed with the Weibull cumulative distribution function. For spacing, probability plot, Laplace-transform, and moment-style statistics, the implementation follows the corresponding formulas in pysatl_criterion.statistics.goodness_of_fit.weibull.
Author(s)¶
Alexey Mironov
References¶
The statistic follows the implementation in pysatl_criterion.statistics.goodness_of_fit.weibull.
Examples¶
from pysatl_criterion.statistics.goodness_of_fit import (
MSFWeibullGofStatistic,
)
test_statistic = MSFWeibullGofStatistic(a=1, k=2)
statistic_result = test_statistic.execute_statistic([0.42, 0.65, 0.88, 1.12, 1.43, 1.76, 2.05, 2.44, 2.91, 3.37])
print(statistic_result)