statsmodels.discrete.discrete_model.NegativeBinomial.cov_params_func_l1 NegativeBinomial.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.sandbox.regression.gmm.LinearIVGMM.fit LinearIVGMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None) Estimate parameters using GMM and return GMMResults TODO: weight and covariance arguments still need to be made consistent with similar options in other models, see RegressionResult.get_robustcov_results Parameters: start_params : array (optional) starting value for parameters ub m

statsmodels.tsa.arima_model.ARMAResults.maparams static ARMAResults.maparams() [source]

statsmodels.miscmodels.count.PoissonGMLE.predict_distribution PoissonGMLE.predict_distribution(exog) [source] return frozen scipy.stats distribution with mu at estimated prediction

statsmodels.tsa.arima_model.ARMAResults.maroots static ARMAResults.maroots() [source]

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

statsmodels.regression.mixed_linear_model.MixedLMResults.llf static MixedLMResults.llf()

statsmodels.discrete.discrete_model.LogitResults.llr static LogitResults.llr()

statsmodels.discrete.discrete_model.Probit.loglikeobs Probit.loglikeobs(params) [source] Log-likelihood of probit model for each observation Parameters: params : array-like The parameters of the model. Returns: loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at params. See Notes Notes for observations where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.tsa.arima_model.ARMA.fit ARMA.fit(start_params=None, trend='c', method='css-mle', transparams=True, solver='lbfgs', maxiter=50, full_output=1, disp=5, callback=None, **kwargs) [source] Fits ARMA(p,q) model using exact maximum likelihood via Kalman filter. Parameters: start_params : array-like, optional Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. See there for more information. transparams : bool, optional Whehter or not to trans