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_init(self):
"""Test __init__."""
skst = SkStJR(ndim=3)
self.assertIsInstance(skst.eta, np.ndarray)
self.assertIsInstance(skst.lam, np.ndarray)
eta, lam = [10, 15], [.5, 1.5]
skst = SkStJR(ndim=len(lam), eta=eta, lam=lam)
npt.assert_array_equal(skst.eta, np.array(eta))
npt.assert_array_equal(skst.lam, np.array(lam))
eta, lam = [15, 10], [1.5, .5]
skst.from_theta(np.concatenate((eta, lam)))
npt.assert_array_equal(skst.eta, np.array(eta))
npt.assert_array_equal(skst.lam, np.array(lam))
size = (10, len(eta))
data = np.random.normal(size=size)
skst = SkStJR(ndim=len(lam), data=data)
npt.assert_array_equal(skst.data, data)
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()
