statsmodels.discrete.discrete_model.CountResults.llr_pvalue static CountResults.llr_pvalue()

statsmodels.regression.linear_model.GLS.score GLS.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.duration.hazard_regression.PHReg.information PHReg.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.geterrors classmethod KalmanFilter.geterrors(y, k, k_ar, k_ma, k_lags, nobs, Z_mat, m, R_mat, T_mat, paramsdtype) [source] Returns just the errors of the Kalman Filter

statsmodels.discrete.discrete_model.MNLogit.information MNLogit.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.regression.gmm.GMMResults.load classmethod GMMResults.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 :

statsmodels.discrete.discrete_model.DiscreteModel.fit DiscreteModel.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str

statsmodels.miscmodels.count.PoissonGMLE.score PoissonGMLE.score(params) Gradient of log-likelihood evaluated at params

statsmodels.miscmodels.count.PoissonGMLE.reduceparams PoissonGMLE.reduceparams(params)

statsmodels.tsa.ar_model.ARResults.fittedvalues static ARResults.fittedvalues() [source]