EVOLUTION-MANAGER

Edit File: _hypotests.py

from __future__ import division, print_function, absolute_import
from collections import namedtuple
import numpy as np
import warnings
from ._continuous_distns import chi2

Epps_Singleton_2sampResult = namedtuple('Epps_Singleton_2sampResult',
                                        ('statistic', 'pvalue'))

def 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, y : array-like
        The two samples of observations to be tested. Input must not have more
        than one dimension. Samples can have different lengths.
    t : array-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
    -------
    statistic : float
        The test statistic.
    pvalue : float
        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] 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.

"""

x, y, t = np.asarray(x), np.asarray(y), np.asarray(t)
    # check if x and y are valid inputs
    if x.ndim > 1:
        raise ValueError('x must be 1d, but x.ndim equals {}.'.format(x.ndim))
    if y.ndim > 1:
        raise ValueError('y must be 1d, but y.ndim equals {}.'.format(y.ndim))
    nx, ny = len(x), len(y)
    if (nx < 5) or (ny < 5):
        raise ValueError('x and y should have at least 5 elements, but len(x) '
                         '= {} and len(y) = {}.'.format(nx, ny))
    if not np.isfinite(x).all():
        raise ValueError('x must not contain nonfinite values.')
    if not np.isfinite(y).all():
        raise ValueError('y must not contain nonfinite values.')
    n = nx + ny

# check if t is valid
    if t.ndim > 1:
        raise ValueError('t must be 1d, but t.ndim equals {}.'.format(t.ndim))
    if np.less_equal(t, 0).any():
        raise ValueError('t must contain positive elements only.')

# rescale t with semi-iqr as proposed in [1]; import iqr here to avoid
    # circular import
    from scipy.stats import iqr
    sigma = iqr(np.hstack((x, y))) / 2
    ts = np.reshape(t, (-1, 1)) / sigma

# covariance estimation of ES test
    gx = np.vstack((np.cos(ts*x), np.sin(ts*x))).T  # shape = (nx, 2*len(t))
    gy = np.vstack((np.cos(ts*y), np.sin(ts*y))).T
    cov_x = np.cov(gx.T, bias=True)  # the test uses biased cov-estimate
    cov_y = np.cov(gy.T, bias=True)
    est_cov = (n/nx)*cov_x + (n/ny)*cov_y
    est_cov_inv = np.linalg.pinv(est_cov)
    r = np.linalg.matrix_rank(est_cov_inv)
    if r < 2*len(t):
        warnings.warn('Estimated covariance matrix does not have full rank. '
                      'This indicates a bad choice of the input t and the '
                      'test might not be consistent.')  # see p. 183 in [1]_

# compute test statistic w distributed asympt. as chisquare with df=r
    g_diff = np.mean(gx, axis=0) - np.mean(gy, axis=0)
    w = n*np.dot(g_diff.T, np.dot(est_cov_inv, g_diff))

# apply small-sample correction
    if (max(nx, ny) < 25):
        corr = 1.0/(1.0 + n**(-0.45) + 10.1*(nx**(-1.7) + ny**(-1.7)))
        w = corr * w

p = chi2.sf(w, r)

return Epps_Singleton_2sampResult(w, p)