UnobservedComponents.fit(start_params=None, transformed=True, cov_type='opg', cov_kwds=None, method='lbfgs', maxiter=50, full_output=1, disp=5, callback=None, return_params=False, optim_score=None, optim_complex_step=None, optim_hessian=None, **kwargs)
Fits the model by maximum likelihood via Kalman filter.
Parameters: |
start_params : array_like, optional Initial guess of the solution for the loglikelihood maximization. If None, the default is given by Model.start_params. transformed : boolean, optional Whether or not cov_type : str, optional The
cov_kwds : dict or None, optional A dictionary of arguments affecting covariance matrix computation. opg, oim, approx, robust, robust_approx
method : str, optional The
The explicit arguments in maxiter : int, optional The maximum number of iterations to perform. full_output : boolean, optional Set to True to have all available output in the Results object’s mle_retvals attribute. The output is dependent on the solver. See LikelihoodModelResults notes section for more information. disp : boolean, optional Set to True to print convergence messages. callback : callable callback(xk), optional Called after each iteration, as callback(xk), where xk is the current parameter vector. return_params : boolean, optional Whether or not to return only the array of maximizing parameters. Default is False. optim_score : {‘harvey’, ‘approx’} or None, optional The method by which the score vector is calculated. ‘harvey’ uses the method from Harvey (1989), ‘approx’ uses either finite difference or complex step differentiation depending upon the value of optim_complex_step : bool, optional Whether or not to use complex step differentiation when approximating the score; if False, finite difference approximation is used. Default is True. This keyword is only relevant if optim_hessian : {‘opg’,’oim’,’approx’}, optional The method by which the Hessian is numerically approximated. ‘opg’ uses outer product of gradients, ‘oim’ uses the information matrix formula from Harvey (1989), and ‘approx’ uses numerical approximation. This keyword is only relevant if the optimization method uses the Hessian matrix. **kwargs Additional keyword arguments to pass to the optimizer. |
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Returns: |
MLEResults |
See also
statsmodels.base.model.LikelihoodModel.fit
, MLEResults
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.structural.UnobservedComponents.fit.html