%feature("docstring") OT::Wilks "Class to evaluate the Wilks number. Refer to :ref:`quantile_estimation_wilks`. Parameters ---------- randomVector : :class:`~openturns.RandomVector` of dimension 1 Output variable of interest. Notes ----- This class is a static class which enables the evaluation of the Wilks number: the minimal sample size :math:`N_{\alpha, \beta, i}` to perform in order to guarantee that the empirical quantile :math:`\alpha`, noted :math:`\tilde{q}_{\alpha} N_{\alpha, \beta, i}` evaluated with the :math:`(n - i)^{th}` maximum of the sample, noted :math:`X_{n - i}` be greater than the theoretical quantile :math:`q_{\alpha}` with a probability at least :math:`\beta`: .. math:: \Pset (\tilde{q}_{\alpha} N_{\alpha, \beta, i} > q_{\alpha}) > \beta where :math:`\tilde{q}_{\alpha} N_{\alpha, \beta, i} = X_{n-i}`." // --------------------------------------------------------------------- %feature("docstring") OT::Wilks::ComputeSampleSize "Evaluate the size of the sample. Parameters ---------- alpha : positive float :math:`< 1` The order of the quantile we want to evaluate. beta : positive float :math:`< 1` Confidence on the evaluation of the empirical quantile. i : int Rank of the maximum which will evaluate the empirical quantile. Default :math:`i = 0` (maximum of the sample) Returns ------- w : int the Wilks number." // --------------------------------------------------------------------- %feature("docstring") OT::Wilks::computeQuantileBound "Evaluate the bound of the quantile. Parameters ---------- alpha : positive float :math:`< 1` The order of the quantile we want to evaluate. beta : positive float :math:`< 1` Confidence on the evaluation of the empirical quantile. i : int Rank of the maximum which will evaluate the empirical quantile. Default :math:`i = 0` (maximum of the sample) Returns ------- q : :class:`~openturns.Point` The estimate of the quantile upper bound for the given quantile level, at the given confidence level and using the given upper statistics."