AICC

AICC(*args)

Compute the Akaike information criterion (with correction for small data).

Refer to Akaike Information Criterion (AIC).

Parameters:
sample2-d sequence of float

Tested sample.

modelDistribution or DistributionFactory

Tested distribution.

n_parametersint, 0 \leq k, optional

The number of parameters in the distribution that have been estimated from the sample. This parameter must not be provided if a DistributionFactory was provided as the second argument (it will internally be set to the number of parameters estimated by the DistributionFactory). It can be specified if a Distribution was provided as the second argument, but if it is not, it will be set equal to 0.

Returns:
estimatedDistDistribution

Estimated distribution (case factory as argument)

AICCfloat

The Akaike information criterion (corrected).

Notes

This is used for model selection, especially with small data samples. In case we set a factory argument, the method returns both the estimated distribution and AICc value. Otherwise it returns only the AICc value.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> ot.FittingTest.AICC(sample, distribution)
2.793869...
>>> ot.FittingTest.AICC(sample, distribution, 2)
2.942017...
>>> fitted_dist, aicc = ot.FittingTest.AICC(sample, ot.NormalFactory())
>>> aicc
2.932204...