statsmodels.duration.hazard_regression.PHRegResults.initialize PHRegResults.initialize(model, params, **kwd)

statsmodels.tsa.vector_ar.var_model.VARResults.acorr VARResults.acorr(nlags=None) Compute theoretical autocorrelation function Returns: acorr : ndarray (p x k x k)

statsmodels.discrete.discrete_model.LogitResults.summary LogitResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns:

statsmodels.iolib.summary.Summary.as_text Summary.as_text() [source] return tables as string Returns: txt : string summary tables and extra text as one string

statsmodels.tsa.arima_model.ARMA.geterrors ARMA.geterrors(params) [source] Get the errors of the ARMA process. Parameters: params : array-like The fitted ARMA parameters order : array-like 3 item iterable, with the number of AR, MA, and exogenous parameters, including the trend

statsmodels.sandbox.regression.gmm.LinearIVGMM.momcond LinearIVGMM.momcond(params)

statsmodels.discrete.discrete_model.BinaryModel.fit_regularized BinaryModel.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 Initial guess of

statsmodels.regression.linear_model.WLS.whiten WLS.whiten(X) [source] Whitener for WLS model, multiplies each column by sqrt(self.weights) Parameters: X : array-like Data to be whitened Returns: sqrt(weights)*X :

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

statsmodels.discrete.discrete_model.MultinomialResults.f_test MultinomialResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypothese