def simultaneous_cb(self, alpha=0.05, method="hw", transform="log"):
        """
        Returns a simultaneous confidence band for the survival function.

        Arguments
        ---------
        alpha : float
            `1 - alpha` is the desired simultaneous coverage
            probability for the confidence region.  Currently alpha
            must be set to 0.05, giving 95% simultaneous intervals.
        method : string
            The method used to produce the simultaneous confidence
            band.  Only the Hall-Wellner (hw) method is currently
            implemented.
        transform : string
            The used to produce the interval (note that the returned
            interval is on the survival probability scale regardless
            of which transform is used).  Only `log` and `arcsin` are
            implemented.

        Returns
        -------
        lcb : array-like
            The lower confidence limits corresponding to the points
            in `surv_times`.
        ucb : array-like
            The upper confidence limits corresponding to the points
            in `surv_times`.
        """

        method = method.lower()
        if method != "hw":
            raise ValueError("only the Hall-Wellner (hw) method is implemented")

        if alpha != 0.05:
            raise ValueError("alpha must be set to 0.05")

        transform = transform.lower()
        s2 = self.surv_prob_se**2 / self.surv_prob**2
        nn = self.n_risk
        if transform == "log":
            denom = np.sqrt(nn) * np.log(self.surv_prob)
            theta = 1.3581 * (1 + nn * s2) / denom
            theta = np.exp(theta)
            lcb = self.surv_prob**(1/theta)
            ucb = self.surv_prob**theta
        elif transform == "arcsin":
            k = 1.3581
            k *= (1 + nn * s2) / (2 * np.sqrt(nn))
            k *= np.sqrt(self.surv_prob / (self.surv_prob - 1))
            f = np.arcsin(np.sqrt(self.surv_prob))
            v = np.clip(f - k, 0, np.inf)
            lcb = np.sin(v)**2
            v = np.clip(f + k, -np.inf, np.pi/2)
            ucb = np.sin(v)**2
        else:
            raise ValueError("Unknown transform")

        return lcb, ucb
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/duration/survfunc.py/SurvfuncRight.simultaneous_cb/swapped operands of `-`