# scipy.stats.kstwobign¶

scipy.stats.kstwobign(*args, **kwds) = <scipy.stats._continuous_distns.kstwobign_gen object>[source]

Kolmogorov-Smirnov two-sided test for large N.

This is the asymptotic distribution of the two-sided Kolmogorov-Smirnov statistic $$\sqrt{n} D_n$$ that measures the maximum absolute distance of the theoretical CDF from the empirical CDF (see kstest).

As an instance of the rv_continuous class, kstwobign object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.

Notes

$$\sqrt{n} D_n$$ is given by

$D_n = \text{sup}_x |F_n(x) - F(x)|$

where $$F$$ is a CDF and $$F_n$$ is an empirical CDF. kstwobign describes the asymptotic distribution (i.e. the limit of $$\sqrt{n} D_n$$) under the null hypothesis of the KS test that the empirical CDF corresponds to i.i.d. random variates with CDF $$F$$.

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, kstwobign.pdf(x, loc, scale) is identically equivalent to kstwobign.pdf(y) / scale with y = (x - loc) / scale.

References

1

Marsaglia, G. et al. “Evaluating Kolmogorov’s distribution”, Journal of Statistical Software, 8(18), 2003.

Examples

>>> from scipy.stats import kstwobign
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)


Calculate a few first moments:

>>> mean, var, skew, kurt = kstwobign.stats(moments='mvsk')


Display the probability density function (pdf):

>>> x = np.linspace(kstwobign.ppf(0.01),
...                 kstwobign.ppf(0.99), 100)
>>> ax.plot(x, kstwobign.pdf(x),
...        'r-', lw=5, alpha=0.6, label='kstwobign pdf')


Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

Freeze the distribution and display the frozen pdf:

>>> rv = kstwobign()
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')


Check accuracy of cdf and ppf:

>>> vals = kstwobign.ppf([0.001, 0.5, 0.999])
>>> np.allclose([0.001, 0.5, 0.999], kstwobign.cdf(vals))
True


Generate random numbers:

>>> r = kstwobign.rvs(size=1000)


And compare the histogram:

>>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()


Methods

 rvs(loc=0, scale=1, size=1, random_state=None) Random variates. pdf(x, loc=0, scale=1) Probability density function. logpdf(x, loc=0, scale=1) Log of the probability density function. cdf(x, loc=0, scale=1) Cumulative distribution function. logcdf(x, loc=0, scale=1) Log of the cumulative distribution function. sf(x, loc=0, scale=1) Survival function (also defined as 1 - cdf, but sf is sometimes more accurate). logsf(x, loc=0, scale=1) Log of the survival function. ppf(q, loc=0, scale=1) Percent point function (inverse of cdf — percentiles). isf(q, loc=0, scale=1) Inverse survival function (inverse of sf). moment(n, loc=0, scale=1) Non-central moment of order n stats(loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(loc=0, scale=1) (Differential) entropy of the RV. fit(data, loc=0, scale=1) Parameter estimates for generic data. expect(func, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Expected value of a function (of one argument) with respect to the distribution. median(loc=0, scale=1) Median of the distribution. mean(loc=0, scale=1) Mean of the distribution. var(loc=0, scale=1) Variance of the distribution. std(loc=0, scale=1) Standard deviation of the distribution. interval(alpha, loc=0, scale=1) Endpoints of the range that contains alpha percent of the distribution

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