statsmodels.discrete.discrete_model.CountModel.fit_regularized CountModel.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) [source] Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, optional Initial guess of t

statsmodels.discrete.discrete_model.MNLogit.loglike_and_score MNLogit.loglike_and_score(params) [source] Returns log likelihood and score, efficiently reusing calculations. Note that both of these returned quantities will need to be negated before being minimized by the maximum likelihood fitting machinery.

statsmodels.discrete.discrete_model.MultinomialResults.pvalues static MultinomialResults.pvalues()

statsmodels.tsa.vector_ar.var_model.VARResults.summary VARResults.summary() [source] Compute console output summary of estimates Returns: summary : VARSummary

statsmodels.discrete.discrete_model.NegativeBinomial.cdf NegativeBinomial.cdf(X) The cumulative distribution function of the model.

statsmodels.regression.quantile_regression.QuantRegResults.fvalue static QuantRegResults.fvalue()

statsmodels.sandbox.regression.gmm.LinearIVGMM.start_weights LinearIVGMM.start_weights(inv=True)

statsmodels.sandbox.regression.gmm.IVRegressionResults.wresid static IVRegressionResults.wresid()

statsmodels.sandbox.regression.gmm.IVRegressionResults.bic static IVRegressionResults.bic()

statsmodels.tsa.ar_model.ARResults.load classmethod ARResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :