def estimate_bivariate_mle_jr():
ndim = 2
size = (10000, ndim)
data = np.random.normal(size=size)
eta, lam = 4, -.9
skst = SkewStudent(eta=eta, lam=lam)
data = skst.rvs(size=size)
model = SkStJR(ndim=ndim, data=data)
out = model.fit_mle()
print(out)
model.from_theta(out.x)
fig, axes = plt.subplots(nrows=size[1], ncols=1)
for innov, ax in zip(data.T, axes):
sns.kdeplot(innov, ax=ax, label='data')
lines = [ax.get_lines()[0].get_xdata() for ax in axes]
lines = np.vstack(lines).T
marginals = model.marginals(lines)
for line, margin, ax in zip(lines.T, marginals.T, axes):
ax.plot(line, margin, label='fitted')
ax.legend()
plt.show()
def estimate_bivariate_mle_jr():
ndim = 2
size = (10000, ndim)
data = np.random.normal(size=size)
eta, lam = 4, -.9
skst = SkewStudent(eta=eta, lam=lam)
data = skst.rvs(size=size)
model = SkStJR(ndim=ndim, data=data)
out = model.fit_mle()
print(out)
model.from_theta(out.x)
fig, axes = plt.subplots(nrows=size[1], ncols=1)
for innov, ax in zip(data.T, axes):
sns.kdeplot(innov, ax=ax, label='data')
lines = [ax.get_lines()[0].get_xdata() for ax in axes]
lines = np.vstack(lines).T
marginals = model.marginals(lines)
for line, margin, ax in zip(lines.T, marginals.T, axes):
ax.plot(line, margin, label='fitted')
ax.legend()
plt.show()
def test_marginals(self):
"""Test marginals."""
eta, lam = [10, 15, 10], [.5, 1.5, 2]
skst = SkStJR(eta=eta, lam=lam, ndim=len(lam))
size = (10, len(eta))
data = np.random.normal(size=size)
marginals = skst.marginals(data)
self.assertEqual(marginals.ndim, 2)
self.assertEqual(marginals.shape, size)
self.assertGreater(marginals.all(), 0)
