def _gpdfit(x):
    """Estimate the parameters for the Generalized Pareto Distribution (GPD).

    Empirical Bayes estimate for the parameters of the generalized Pareto
    distribution given the data.

    Parameters
    ----------
    x : array
        sorted 1D data array

    Returns
    -------
    k : float
        estimated shape parameter
    sigma : float
        estimated scale parameter
    """
    prior_bs = 3
    prior_k = 10
    len_x = len(x)
    m_est = 30 + int(len_x ** 0.5)

    b_ary = 1 - np.sqrt(m_est / (np.arange(1, m_est + 1, dtype=float) - 0.5))
    b_ary /= prior_bs * x[int(len_x / 4 + 0.5) - 1]
    b_ary += 1 / x[-1]

    k_ary = np.log1p(-b_ary[:, None] * x).mean(axis=1)  # pylint: disable=no-member
    len_scale = len_x * (np.log(-(b_ary / k_ary)) - k_ary - 1)
    weights = 1 / np.exp(len_scale - len_scale[:, None]).sum(axis=1)

    # remove negligible weights
    real_idxs = weights >= 10 * np.finfo(float).eps
    if not np.all(real_idxs):
        weights = weights[real_idxs]
        b_ary = b_ary[real_idxs]
    # normalise weights
    weights /= weights.sum()

    # posterior mean for b
    b_post = np.sum(b_ary * weights)
    # estimate for k
    k_post = np.log1p(-b_post * x).mean()  # pylint: disable=invalid-unary-operand-type,no-member
    # add prior for k_post
    k_post = (len_x * k_post + prior_k * 0.5) / (len_x + prior_k)
    sigma = -k_post / b_post

    return k_post, sigma