Merge pull request statsmodels#8862 from bashtage/statespace-internet-docs

DOC: Small additional fixes

Author: bashtage

docs/source/release/version0.14.rst

@@ -21,8 +21,8 @@ was already in statsmodels main and how much usage it has already
 seen.  If there are specific known problems or limitations, then they
 are mentioned in the docstrings.
 
-Stats
------
+Github Stats
+------------
 **Issues Closed**: 248
 
 **Pull Requests Merged**: 335

statsmodels/distributions/copula/copulas.py

@@ -64,7 +64,7 @@ def rvs(self, nobs=1, cop_args=None, marg_args=None, random_state=None):
             to be a list of tuples with the same length has the number of
             marginal distributions. The list can contain empty tuples for
             marginal distributions that do not take parameter arguments.
-        random_state : {None, int, `numpy.random.Generator`}, optional
+        random_state : {None, int, numpy.random.Generator}, optional
             If `seed` is None then the legacy singleton NumPy generator.
             This will change after 0.13 to use a fresh NumPy ``Generator``,
             so you should explicitly pass a seeded ``Generator`` if you
@@ -277,7 +277,7 @@ def rvs(self, nobs=1, args=(), random_state=None):
         args : tuple
             Arguments for copula parameters. The number of arguments depends
             on the copula.
-        random_state : {None, int, `numpy.random.Generator`}, optional
+        random_state : {None, int, numpy.random.Generator}, optional
             If `seed` is None then the legacy singleton NumPy generator.
             This will change after 0.13 to use a fresh NumPy ``Generator``,
             so you should explicitly pass a seeded ``Generator`` if you
@@ -371,7 +371,7 @@ def plot_scatter(self, sample=None, nobs=500, random_state=None, ax=None):
             is generated.
         nobs : int, optional
             Number of samples to generate from the copula.
-        random_state : {None, int, `numpy.random.Generator`}, optional
+        random_state : {None, int, numpy.random.Generator}, optional
             If `seed` is None then the legacy singleton NumPy generator.
             This will change after 0.13 to use a fresh NumPy ``Generator``,
             so you should explicitly pass a seeded ``Generator`` if you

statsmodels/robust/norms.py

@@ -406,9 +406,12 @@ def rho(self, z):
         Returns
         -------
         rho : ndarray
-            rho(z) = a**2 *(1-cos(z/a))     for \|z\| <= a*pi
+            The elements of rho are defined as:
 
-            rho(z) = 2*a                    for \|z\| > a*pi
+            .. math::
+
+                rho(z) & = a^2 *(1-cos(z/a)), |z| \leq a\pi \\
+                rho(z) & = 2a, |z|>q\pi
         """
 
         a = self.a
@@ -878,7 +881,7 @@ class MQuantileNorm(RobustNorm):
     in QuantileRegression but replaces the L1 absolute value by a chosen
     base norm.
 
-        rho_q(u) = |q - I(q < 0)| * rho_base(u)
+        rho_q(u) = abs(q - I(q < 0)) * rho_base(u)
 
     or, equivalently,
 
@@ -919,8 +922,6 @@ class MQuantileNorm(RobustNorm):
     .. [*] Newey, Whitney K., and James L. Powell. 1987. “Asymmetric Least
        Squares Estimation and Testing.” Econometrica 55 (4): 819–47.
        doi:10.2307/1911031.
-    ...
-
     """
 
     def __init__(self, q, base_norm):

statsmodels/tsa/statespace/kalman_filter.py

@@ -1168,8 +1168,7 @@ def simulate(self, nsimulations, measurement_shocks=None,
             Whether or not to return the simulator object. Typically used to
             improve performance when performing repeated sampling. Default is
             False.
-        random_state : {None, int, `numpy.random.Generator`,
-                        `numpy.random.RandomState`}, optional
+        random_state : {None, int, Generator, RandomState}, optionall
             If `seed` is None (or `np.random`), the `numpy.random.RandomState`
             singleton is used.
             If `seed` is an int, a new ``RandomState`` instance is used,

statsmodels/tsa/statespace/simulation_smoother.py

@@ -24,13 +24,13 @@ def check_random_state(seed=None):
     """Turn `seed` into a `numpy.random.Generator` instance.
     Parameters
     ----------
-    seed : {None, int, `numpy.random.Generator`,
-            `numpy.random.RandomState`}, optional
-        If `seed` is None the `numpy.random.Generator` singleton is used.
-        If `seed` is an int, a new ``Generator`` instance is used,
-        seeded with `seed`.
-        If `seed` is already a ``Generator`` or ``RandomState`` instance then
-        that instance is used.
+    seed : {None, int, Generator, RandomState}, optional
+        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+        singleton is used.
+        If `seed` is an int, a new ``numpy.random.RandomState`` instance
+        is used, seeded with `seed`.
+        If `seed` is already a ``numpy.random.Generator`` or
+        ``numpy.random.RandomState`` instance then that instance is used.
     Returns
     -------
     seed : {`numpy.random.Generator`, `numpy.random.RandomState`}
@@ -210,14 +210,13 @@ class must extend from `SimulationSmoothResults`.
             than -1, only simulation will be performed (i.e. simulation
             smoothing will not be performed), so that only the `generated_obs`
             and `generated_state` attributes will be available.
-        random_state : {None, int, `numpy.random.Generator`,
-                        `numpy.random.RandomState`}, optional
+        random_state : {None, int, Generator, RandomState}, optional
             If `seed` is None (or `np.random`), the `numpy.random.RandomState`
             singleton is used.
-            If `seed` is an int, a new ``RandomState`` instance is used,
-            seeded with `seed`.
-            If `seed` is already a ``Generator`` or ``RandomState`` instance
-            then that instance is used.
+            If `seed` is an int, a new ``numpy.random.RandomState`` instance
+            is used, seeded with `seed`.
+            If `seed` is already a ``numpy.random.Generator`` or
+            ``numpy.random.RandomState`` instance then that instance is used.
         **kwargs
             Additional keyword arguments, used to set the simulation output.
             See `set_simulation_output` for more details.
@@ -298,14 +297,13 @@ class SimulationSmoothResults:
         A Statespace representation
     simulation_smoother : {{prefix}}SimulationSmoother object
         The Cython simulation smoother object with which to simulation smooth.
-    random_state : {None, int, `numpy.random.Generator`,
-                        `numpy.random.RandomState`}, optional
+    random_state : {None, int, Generator, RandomState}, optional
         If `seed` is None (or `np.random`), the `numpy.random.RandomState`
         singleton is used.
-        If `seed` is an int, a new ``RandomState`` instance is used,
-        seeded with `seed`.
-        If `seed` is already a ``Generator`` or ``RandomState`` instance
-        then that instance is used.
+        If `seed` is an int, a new ``numpy.random.RandomState`` instance
+        is used, seeded with `seed`.
+        If `seed` is already a ``numpy.random.Generator`` or
+        ``numpy.random.RandomState`` instance then that instance is used.
 
     Attributes
     ----------
@@ -606,14 +604,13 @@ def simulate(self, simulation_output=-1,
             then it is assumed to contain draws from the standard Normal
             distribution that must be transformed using the `initial_state_cov`
             covariance matrix. Default is False.
-        random_state : {None, int, `numpy.random.Generator`,
-                        `numpy.random.RandomState`}, optional
+        random_state : {None, int, Generator, RandomState}, optional
             If `seed` is None (or `np.random`), the `numpy.random.RandomState`
             singleton is used.
-            If `seed` is an int, a new ``RandomState`` instance is used,
-            seeded with `seed`.
-            If `seed` is already a ``Generator`` or ``RandomState`` instance
-            then that instance is used.
+            If `seed` is an int, a new ``numpy.random.RandomState`` instance
+            is used, seeded with `seed`.
+            If `seed` is already a ``numpy.random.Generator`` or
+            ``numpy.random.RandomState`` instance then that instance is used.
         disturbance_variates : bool, optional
             Deprecated, please use pretransformed_measurement_shocks and
             pretransformed_state_shocks instead.