Min-Toshiyuki test for gamma distribution

Description

Performs Min-Toshiyuki tail-sensitive goodness-of-fit test for the hypothesis that the sample comes from a gamma distribution. The statistic up-weights empirical distribution deviations near the tails.

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 (
    MinToshiyukiGammaGofStatistic,
)


test_statistic = MinToshiyukiGammaGofStatistic(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 transforms observations with the gamma cumulative distribution function and passes the resulting values to the common Min-Toshiyuki statistic implementation.

Author(s)

Sergey Golovachev, Alexey Mironov

References

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

Examples

from pysatl_criterion.statistics.goodness_of_fit import (
    MinToshiyukiGammaGofStatistic,
)


test_statistic = MinToshiyukiGammaGofStatistic(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)