statsmodels.tsa.arima_model.ARIMA.loglike_css ARIMA.loglike_css(params, set_sigma2=True) Conditional Sum of Squares likelihood function.

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

statsmodels.iolib.table.SimpleTable.append SimpleTable.append() L.append(object) ? append object to end

statsmodels.sandbox.distributions.extras.ACSkewT_gen.est_loc_scale ACSkewT_gen.est_loc_scale(*args, **kwds) est_loc_scale is deprecated! This function is deprecated, use self.fit_loc_scale(data) instead.

statsmodels.genmod.generalized_estimating_equations.GEE.estimate_scale GEE.estimate_scale() [source] Returns an estimate of the scale parameter phi at the current parameter value.

statsmodels.sandbox.sysreg.SUR.initialize SUR.initialize() [source]

statsmodels.sandbox.regression.gmm.IV2SLS.score IV2SLS.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.regression.linear_model.GLSAR.fit_regularized GLSAR.fit_regularized(method='coord_descent', maxiter=1000, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-08, zero_tol=1e-08, **kwargs) Return a regularized fit to a linear regression model. Parameters: method : string Only the coordinate descent algorithm is implemented. maxiter : integer The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha : scalar or ar

statsmodels.iolib.table.SimpleTable.insert_stubs SimpleTable.insert_stubs(loc, stubs) [source] Return None. Insert column of stubs at column loc. If there is a header row, it gets an empty cell. So len(stubs) should equal the number of non-header rows.

statsmodels.discrete.discrete_model.MultinomialModel.fit_regularized MultinomialModel.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 Initia