def _calculate_robust_covariance(self):
    'Calculate robust variance covariance matrices for our two sets of coefficients\n        '
    (_v0, _v1) = self._calculate_v()
    (_w0, _w1) = self._calculate_w()
    _robust_cov0 = ((np.linalg.inv(np.dot(np.dot(self._x0.T, _v0), self._x0)) * np.dot(np.dot(self._x0.T, _w0), self._x0)) * np.linalg.inv(np.dot(np.dot(self._x0.T, _v0), self._x0)))
    _robust_cov1 = ((np.linalg.inv(np.dot(np.dot(self._x1.T, _v1), self._x1)) * np.dot(np.dot(self._x1.T, _w1), self._x1)) * np.linalg.inv(np.dot(np.dot(self._x1.T, _v1), self._x1)))
    return (_robust_cov0, _robust_cov1)
Calculate robust variance covariance matrices for our two sets of coefficients
def _calculate_robust_covariance(self):
    '\n        '
    (_v0, _v1) = self._calculate_v()
    (_w0, _w1) = self._calculate_w()
    _robust_cov0 = ((np.linalg.inv(np.dot(np.dot(self._x0.T, _v0), self._x0)) * np.dot(np.dot(self._x0.T, _w0), self._x0)) * np.linalg.inv(np.dot(np.dot(self._x0.T, _v0), self._x0)))
    _robust_cov1 = ((np.linalg.inv(np.dot(np.dot(self._x1.T, _v1), self._x1)) * np.dot(np.dot(self._x1.T, _w1), self._x1)) * np.linalg.inv(np.dot(np.dot(self._x1.T, _v1), self._x1)))
    return (_robust_cov0, _robust_cov1)