def METHOD_NAME(F, alpha=.05):
    r"""
    Constructs a Dvoretzky-Kiefer-Wolfowitz confidence band for the eCDF.
    Parameters
    ----------
    F : array_like
        The empirical distributions
    alpha : float
        Set alpha for a (1 - alpha) % confidence band.
    Notes
    -----
    Based on the DKW inequality.
    .. math:: P \left( \sup_x \left| F(x) - \hat(F)_n(X) \right| >
       \epsilon \right) \leq 2e^{-2n\epsilon^2}
    References
    ----------
    Wasserman, L. 2006. `All of Nonparametric Statistics`. Springer.
    """
    nobs = len(F)
    epsilon = np.sqrt(np.log(2./alpha) / (2 * nobs))
    lower = np.clip(F - epsilon, 0, 1)
    upper = np.clip(F + epsilon, 0, 1)
    return lower, upper