def lambda_(self): df = self._df rho = self._rhos if np.isinf(df): res = (self._rhos == 1).astype(float) else: res = 2 * t.cdf(-np.sqrt((df + 1) * (1 - rho)), df=df + 1) return TailDep(res, res)
def lambda_(self): return TailDep(0, 2 * 2**(1 / self.params))
def lambda_(self): res = (self._rhos == 1).astype(float) return TailDep(res, res)
def lambda_(self): return TailDep(0, 0)
def lambda_(self): # pragma: no cover return TailDep(0, 0)
def lambda_(self): if np.isnan(self._theta): return TailDep(self._theta, self._theta) return TailDep(2**(-1 / self._theta) if self._theta > 0 else 0, 0)