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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/tests/
H A Dtest_mlemodel.py450 mod = MLEModel([1, 2], **kwargs)
481 mod = MLEModel(endog, **kwargs)
535 mod = MLEModel(endog, **kwargs)
577 mod = MLEModel(endog, **kwargs)
603 mod = MLEModel(endog, **kwargs)
631 mod = MLEModel([1], **kwargs)
635 mod = MLEModel([1.], **kwargs)
643 mod = MLEModel(['a'], **kwargs)
667 mod = MLEModel(endog, **kwargs)
883 mod = MLEModel(
[all …]
H A Dtest_multivariate_switch_univariate.py40 mod = mlemodel.MLEModel(endog, k_states=1, k_posdef=1)
58 mod = mlemodel.MLEModel(endog, k_states=3, k_posdef=2)
84 if isinstance(mod, mlemodel.MLEModel):
143 if isinstance(mod, mlemodel.MLEModel):
H A Dtest_simulation_smoothing.py53 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
223 generated_model = mlemodel.MLEModel(
326 generated_model = mlemodel.MLEModel(
454 mod = mlemodel.MLEModel(obs, k_states=2, k_posdef=2, **kwargs)
502 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
H A Dtest_univariate.py23 from statsmodels.tsa.statespace.mlemodel import MLEModel
58 cls.mlemodel = MLEModel(data, k_states=k_states, **kwargs)
279 mod = MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
540 mod = MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
H A Dtest_models.py21 class Intercepts(mlemodel.MLEModel):
196 class LargeStateCovAR1(mlemodel.MLEModel):
H A Dtest_collapsed.py18 from statsmodels.tsa.statespace.mlemodel import MLEModel
49 cls.mlemodel = MLEModel(data, k_states=k_states, **kwargs)
440 mod = MLEModel(obs, k_states=2, k_posdef=2, **kwargs)
512 mod = MLEModel(obs, k_states=4, k_posdef=2, **kwargs)
H A Dtest_fixed_params.py22 mod = mlemodel.MLEModel([], 1)
36 mod = mlemodel.MLEModel([], 1)
136 mod = mlemodel.MLEModel([], 1)
H A Dtest_kalman.py31 from statsmodels.tsa.statespace.mlemodel import MLEModel
736 mod = MLEModel(endog, k_states=1, k_posdef=1)
770 mod = MLEModel(endog, k_states=2, k_posdef=2)
H A Dtest_smoothing.py396 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
582 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
765 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
936 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
957 mod = mlemodel.MLEModel(obs, k_states=6, k_posdef=3, **kwargs)
H A Dtest_cfa_tvpvar.py63 class TVPVAR(mlemodel.MLEModel):
/dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/
H A Dstatespace_concentrated_scale.py79 class LocalLevel(sm.tsa.statespace.MLEModel):
152 class LocalLevelConcentrated(sm.tsa.statespace.MLEModel):
H A Dstatespace_custom_models.py106 class TVRegression(sm.tsa.statespace.MLEModel):
345 class TVRegressionExtended(sm.tsa.statespace.MLEModel):
641 class MultipleYsModel(sm.tsa.statespace.MLEModel):
H A Dstatespace_local_linear_trend.py127 class LocalLinearTrend(sm.tsa.statespace.MLEModel):
H A Dstatespace_tvpvar_mcmc_cfa.py376 class TVPVAR(sm.tsa.statespace.MLEModel):
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/
H A Dapi.py5 from .mlemodel import MLEModel, MLEResults
H A Dexponential_smoothing.py28 from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
31 class ExponentialSmoothing(MLEModel):
559 @Appender(MLEModel.loglike.__doc__)
570 @Appender(MLEModel.filter.__doc__)
587 @Appender(MLEModel.smooth.__doc__)
H A D_quarterly_ar1.py19 class QuarterlyAR1(mlemodel.MLEModel):
H A Dmlemodel.py86 class MLEModel(tsbase.TimeSeriesModel): class
138 super(MLEModel, self).__init__(endog=endog, exog=exog,
246 kwds = super(MLEModel, self)._get_init_kwds()
704 mlefit = super(MLEModel, self).fit(start_params, method=method,
928 MLEModel._loglike_param_names, MLEModel._loglike_param_defaults,
1358 _handle_args(MLEModel._score_param_names,
1359 MLEModel._score_param_defaults, *args, **kwargs))
1490 _handle_args(MLEModel._hessian_param_names,
1491 MLEModel._hessian_param_defaults,
H A Dvarmax.py23 from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
32 class VARMAX(MLEModel):
809 @Appender(MLEModel.simulate.__doc__)
/dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/
H A Dstatespace.rst380 mlemodel.MLEModel
386 `SARIMAXResults` classes, which are built by extending `MLEModel` and
389 In simple cases, the model can be constructed entirely using the MLEModel
410 class AR2(sm.tsa.statespace.MLEModel):
549 - :py:meth:`fit <mlemodel.MLEModel.fit>` - estimate parameters via maximum
553 - :py:meth:`smooth <mlemodel.MLEModel.smooth>` - return a results object
561 - :py:meth:`param_names <mlemodel.MLEModel.param_names>` - names of the
563 - :py:meth:`state_names <mlemodel.MLEModel.state_names>` - names of the
571 - :py:meth:`filter <mlemodel.MLEModel.filter>` - return a results object
745 The :py:meth:`fit_constrained <mlemodel.MLEModel.fit_constrained>` method
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/dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/release/
H A Dversion0.10.2.rst42 * :pr:`6050`: BUG: MLEModel now passes nobs to Representation
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/regression/
H A Drecursive_ls.py15 MLEModel, MLEResults, MLEResultsWrapper, PredictionResults,
30 class RecursiveLS(MLEModel):
136 return super(MLEModel, cls).from_formula(formula, data, subset,
/dports/math/py-statsmodels/statsmodels-0.13.1/examples/notebooks/
H A Dstatespace_local_linear_trend.ipynb70 …n, we create a new class which extends from `statsmodels.tsa.statespace.MLEModel`. There are a num…
98 "class LocalLinearTrend(sm.tsa.statespace.MLEModel):\n",
179 …"Since we defined the local linear trend model as extending from `MLEModel`, the `fit()` method is…
H A Dstatespace_concentrated_scale.ipynb81 "class LocalLevel(sm.tsa.statespace.MLEModel):\n",
177 "class LocalLevelConcentrated(sm.tsa.statespace.MLEModel):\n",
H A Dstatespace_custom_models.ipynb9 …dels. This notebook shows various statespace models that subclass `sm.tsa.statespace.MLEModel`.\n",
108 "class TVRegression(sm.tsa.statespace.MLEModel):\n",
368 "class TVRegressionExtended(sm.tsa.statespace.MLEModel):\n",
666 "class MultipleYsModel(sm.tsa.statespace.MLEModel):\n",

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