def smoothvar(self, xs, ys, x):
        xs, ys = self.in_domain(xs, ys, x)

        if len(xs) > 0:
            fittedvals = np.array([self.smooth(xs, ys, xx) for xx in xs])
            rs = square(subtract(ys, fittedvals))
            w = np.sum(square(subtract(1.0, square(divide(subtract(xs, x),
                                                        self.h)))))
            v = np.sum(multiply(rs, square(subtract(1, square(divide(
                                                subtract(xs, x), self.h))))))
            return v / w
        else:
            return np.nan
Returns the kernel smoothing estimate of the variance at point x.
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/sandbox/nonparametric/kernels.py/Biweight.smoothvar