statsmodels.regression.linear_model.GLSAR.hessian GLSAR.hessian(params) The Hessian matrix of the model

statsmodels.discrete.discrete_model.Probit.loglike Probit.loglike(params) [source] Log-likelihood of probit model (i.e., the normal distribution). Parameters: params : array-like The parameters of the model. Returns: loglike : float The log-likelihood function of the model evaluated at params. See notes. Notes Where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.discrete.discrete_model.Poisson.cdf Poisson.cdf(X) [source] Poisson model cumulative distribution function Parameters: X : array-like X is the linear predictor of the model. See notes. Returns: The value of the Poisson CDF at each point. : Notes The CDF is defined as where assumes the loglinear model. I.e., The parameter X is in the above formula.

statsmodels.discrete.discrete_model.MultinomialResults.aic static MultinomialResults.aic() [source]

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.predict NonlinearIVGMM.predict(params, exog=None) [source]

statsmodels.graphics.gofplots.ProbPlot.sample_percentiles static ProbPlot.sample_percentiles() [source]

statsmodels.discrete.discrete_model.NegativeBinomialResults.initialize NegativeBinomialResults.initialize(model, params, **kwd)

statsmodels.discrete.discrete_model.NegativeBinomialResults.save NegativeBinomialResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to N

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

statsmodels.discrete.discrete_model.NegativeBinomialResults.cov_params NegativeBinomialResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other.