statsmodels.sandbox.regression.gmm.IVGMM.momcond IVGMM.momcond(params) [source]

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

statsmodels.sandbox.regression.gmm.IV2SLS.whiten IV2SLS.whiten(X) [source]

statsmodels.duration.hazard_regression.PHRegResults class statsmodels.duration.hazard_regression.PHRegResults(model, params, cov_params, covariance_type='naive') [source] Class to contain results of fitting a Cox proportional hazards survival model. PHregResults inherits from statsmodels.LikelihoodModelResults Parameters: See statsmodels.LikelihoodModelResults : Returns: **Attributes** : model : class instance PHreg model instance that called fit. normalized_cov_params : array The samp

statsmodels.tsa.arima_model.ARIMAResults.cov_params ARIMAResults.cov_params()

statsmodels.genmod.generalized_estimating_equations.GEEResults.t_test GEEResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple :

statsmodels.regression.linear_model.OLSResults.bse static OLSResults.bse()

statsmodels.regression.linear_model.RegressionResults.nobs static RegressionResults.nobs() [source]

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

statsmodels.tsa.vector_ar.var_model.VARResults.info_criteria static VARResults.info_criteria() [source] information criteria for lagorder selection