def transform_tev(t, rho, df):
'''t-EV model of Demarta and McNeil 2005
restrictions:
- rho in (-1,1)
- x > 0
'''
x = df # alias, Genest and Segers use chi, copual package uses df
def _check_args(rho, x):
cond1 = (x > 0)
cond2 = (rho > 0) and (rho < 1)
return cond1 and cond2
if not np.all(_check_args(rho, x)):
raise ValueError('invalid args')
from scipy.stats import t as stats_t
# use special if I want to avoid stats import
term1 = (np.power(t/(1.-t), 1./x) - rho) # for t
term2 = (np.power((1.-t)/t, 1./x) - rho) # for 1-t
term0 = np.sqrt(1. + x) / np.sqrt(1 - rho*rho)
z1 = term0 * term1
z2 = term0 * term2
transf = t * stats_t._cdf(z1, x+1) + (1 - t) * stats_t._cdf(z2, x+1)
return transf
def _argcheck(self, df, a, b):
self.a = a
self.b = b
self._nb = t._cdf(b, df)
self._na = t._cdf(a, df)
self._delta = self._nb - self._na
self._logdelta = np.log(self._delta)
return (a != b)
def evaluate(self, t, rho, df):
x = df # alias, Genest and Segers use chi, copual package uses df
# if not np.all(self, _check_args(rho, x)):
# raise ValueError('invalid args')
from scipy.stats import t as stats_t
# use special if I want to avoid stats import
term1 = (np.power(t / (1. - t), 1. / x) - rho) # for t
term2 = (np.power((1. - t) / t, 1. / x) - rho) # for 1-t
term0 = np.sqrt(1. + x) / np.sqrt(1 - rho * rho)
z1 = term0 * term1
z2 = term0 * term2
transf = t * stats_t._cdf(z1, x + 1) + (1 - t) * stats_t._cdf(
z2, x + 1)
return transf
def transform_tev(t, rho, x):
'''t-EV model of Demarta and McNeil 2005
restrictions:
- rho in (-1,1)
- x > 0
'''
def _check_args(rho, x):
cond1 = (x > 0)
cond2 = (rho > 0) and (rho < 1)
return cond1 and cond2
if not np.all(_check_args(rho, x)):
raise ValueError('invalid args')
from scipy.stats import t as stats_t #use special if I want to avoid stats import
z = np.sqrt(1. + x) * (np.power(t / (1. - t), 1. / x) - rho)
z /= np.sqrt(1 - rho * rho)
transf = (1 - t) * stats_t._cdf(z, x + 1) + t * stats_t._cdf(z, x + 1)
return transf
def transform_tev(t, rho, x):
'''t-EV model of Demarta and McNeil 2005
restrictions:
- rho in (-1,1)
- x > 0
'''
def _check_args(rho, x):
cond1 = (x > 0)
cond2 = (rho > 0) and (rho < 1)
return cond1 and cond2
if not np.all(_check_args(rho, x)):
raise ValueError('invalid args')
from scipy.stats import t as stats_t #use special if I want to avoid stats import
z = np.sqrt(1. + x) * (np.power(t/(1.-t), 1./x) - rho)
z /= np.sqrt(1 - rho*rho)
transf = (1 - t) * stats_t._cdf(z, x+1) + t * stats_t._cdf(z, x+1)
return transf
def _cdf(self, x, df, a, b):
result = np.zeros_like(x)
mask = (x >= a) * (x <= b)
result[mask] = (t._cdf(x[mask], df) - self._na) / self._delta
result[x > b] = 1.
return result