statsmodels.tsa.ar_model.ARResults.sigma2 static ARResults.sigma2() [source]

statsmodels.regression.linear_model.GLS class statsmodels.regression.linear_model.GLS(endog, exog, sigma=None, missing='none', hasconst=None, **kwargs) [source] Generalized least squares model with a general covariance structure. Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by

statsmodels.sandbox.regression.gmm.GMMResults.llf static GMMResults.llf()

statsmodels.discrete.discrete_model.NegativeBinomial.fit_regularized NegativeBinomial.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]

statsmodels.sandbox.stats.multicomp.StepDown.check_set StepDown.check_set(indices) [source] check whether pairwise distances of indices satisfy condition

statsmodels.sandbox.stats.multicomp.StepDown.run StepDown.run(alpha) [source] main function to run the test, could be done in __call__ instead this could have all the initialization code

statsmodels.sandbox.stats.multicomp.line statsmodels.sandbox.stats.multicomp.line = '' str(object=??) -> string Return a nice string representation of the object. If the argument is a string, the return value is the same object.

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

statsmodels.discrete.discrete_model.MultinomialResults.normalized_cov_params MultinomialResults.normalized_cov_params()

statsmodels.sandbox.regression.gmm.IVGMMResults.llf static IVGMMResults.llf()