statsmodels.sandbox.regression.gmm.GMMResults.get_bse GMMResults.get_bse(**kwds) [source] standard error of the parameter estimates with options Parameters: kwds : optional keywords options for calculating cov_params Returns: bse : ndarray estimated standard error of parameter estimates

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

statsmodels.discrete.discrete_model.BinaryResults.llr_pvalue static BinaryResults.llr_pvalue()

statsmodels.stats.outliers_influence.OLSInfluence class statsmodels.stats.outliers_influence.OLSInfluence(results) [source] class to calculate outlier and influence measures for OLS result Parameters: results : Regression Results instance currently assumes the results are from an OLS regression Notes One part of the results can be calculated without any auxiliary regression (some of which have the _internal postfix in the name. Other statistics require leave-one-observation-out (LOOO) au

statsmodels.sandbox.regression.gmm.GMM.predict GMM.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.

statsmodels.tsa.arima_model.ARMAResults.save ARMAResults.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 None. Notes If remove_data is t

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

statsmodels.nonparametric.kde.KDEUnivariate class statsmodels.nonparametric.kde.KDEUnivariate(endog) [source] Univariate Kernel Density Estimator. Parameters: endog : array-like The variable for which the density estimate is desired. See also KDEMultivariate, kdensity, kdensityfft Notes If cdf, sf, cumhazard, or entropy are computed, they are computed based on the definition of the kernel rather than the FFT approximation, even if the density is fit with FFT = True. KDEUnivariate is mu

statsmodels.robust.norms.HuberT.psi_deriv HuberT.psi_deriv(z) [source] The derivative of Huber?s t psi function Notes Used to estimate the robust covariance matrix.

Installation Using setuptools To obtain the latest released version of statsmodels using setuptools: easy_install -U statsmodels Or follow this link to our PyPI page. Obtaining the Source We do not release very often but the master branch of our source code is usually fine for everyday use. You can get the latest source from our github repository. Or if you have git installed: git clone git://github.com/statsmodels/statsmodels.git If you want to keep up to date with the source on github ju