DiscreteModel.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=True, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, qc_verbose=False, **kwargs)
[source]
Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method.
Parameters: |
|
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Extra parameters are not penalized if alpha is given as a scalar. An example is the shape parameter in NegativeBinomial nb1
and nb2
.
Optional arguments for the solvers (available in Results.mle_settings):
'l1' acc : float (default 1e-6) Requested accuracy as used by slsqp 'l1_cvxopt_cp' abstol : float absolute accuracy (default: 1e-7). reltol : float relative accuracy (default: 1e-6). feastol : float tolerance for feasibility conditions (default: 1e-7). refinement : int number of iterative refinement steps when solving KKT equations (default: 1).
Optimization methodology
With \(L\) the negative log likelihood, we solve the convex but non-smooth problem
via the transformation to the smooth, convex, constrained problem in twice as many variables (adding the “added variables” \(u_k\))
subject to
With \(\partial_k L\) the derivative of \(L\) in the \(k^{th}\) parameter direction, theory dictates that, at the minimum, exactly one of two conditions holds:
© 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.discrete.discrete_model.DiscreteModel.fit_regularized.html