statsmodels.miscmodels.count.PoissonZiGMLE.initialize PoissonZiGMLE.initialize()

statsmodels.tsa.arima_model.ARIMAResults.hqic static ARIMAResults.hqic()

statsmodels.discrete.discrete_model.BinaryResults class statsmodels.discrete.discrete_model.BinaryResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for binary data Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criterio

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

statsmodels.tsa.arima_model.ARMA.initialize ARMA.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

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

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

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.score NonlinearIVGMM.score(params, weights, **kwds) [source]

statsmodels.discrete.discrete_model.NegativeBinomialResults.wald_test NegativeBinomialResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. 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 hypotheses to test can be given as a string. See the

statsmodels.discrete.discrete_model.BinaryResults.t_test BinaryResults.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 : A tuple o