scipy.stats.epps_singleton_2samp

scipy.stats.epps_singleton_2samp(x, y, t=(0.4, 0.8))

Compute the Epps-Singleton (ES) test statistic.

Test the null hypothesis that two samples have the same underlying probability distribution.

Parameters

x, yarray-like

The two samples of observations to be tested. Input must not have more than one dimension. Samples can have different lengths.

tarray-like, optional

The points (t1, …, tn) where the empirical characteristic function is to be evaluated. It should be positive distinct numbers. The default value (0.4, 0.8) is proposed in [1]. Input must not have more than one dimension.

Returns

statisticfloat

The test statistic.

pvaluefloat

The associated p-value based on the asymptotic chi2-distribution.

See also

ks_2samp, anderson_ksamp

Notes

Testing whether two samples are generated by the same underlying distribution is a classical question in statistics. A widely used test is the Kolmogorov-Smirnov (KS) test which relies on the empirical distribution function. Epps and Singleton introduce a test based on the empirical characteristic function in [1].

One advantage of the ES test compared to the KS test is that is does not assume a continuous distribution. In [1], the authors conclude that the test also has a higher power than the KS test in many examples. They recommend the use of the ES test for discrete samples as well as continuous samples with at least 25 observations each, whereas anderson_ksamp is recommended for smaller sample sizes in the continuous case.

The p-value is computed from the asymptotic distribution of the test statistic which follows a chi2 distribution. If the sample size of both x and y is below 25, the small sample correction proposed in [1] is applied to the test statistic.

The default values of t are determined in [1] by considering various distributions and finding good values that lead to a high power of the test in general. Table III in [1] gives the optimal values for the distributions tested in that study. The values of t are scaled by the semi-interquartile range in the implementation, see [1].

References

1(1,2,3,4,5,6,7)

T. W. Epps and K. J. Singleton, “An omnibus test for the two-sample problem using the empirical characteristic function”, Journal of Statistical Computation and Simulation 26, p. 177–203, 1986.

2

S. J. Goerg and J. Kaiser, “Nonparametric testing of distributions - the Epps-Singleton two-sample test using the empirical characteristic function”, The Stata Journal 9(3), p. 454–465, 2009.

scipy.stats.ks_2samp scipy.stats.mannwhitneyu