statsmodels.tsa.arima_model.ARIMAResults.mafreq static ARIMAResults.mafreq() Returns the frequency of the MA roots. This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots.

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.momcond_mean NonlinearIVGMM.momcond_mean(params) mean of moment conditions,

statsmodels.sandbox.regression.try_catdata.groupsstats_1d statsmodels.sandbox.regression.try_catdata.groupsstats_1d(y, x, labelsunique) [source] use ndimage to get fast mean and variance

statsmodels.robust.robust_linear_model.RLMResults.bcov_scaled static RLMResults.bcov_scaled() [source]

statsmodels.iolib.summary.Summary.add_table_2cols Summary.add_table_2cols(res, title=None, gleft=None, gright=None, yname=None, xname=None) [source] add a double table, 2 tables with one column merged horizontally Parameters: res : results instance some required information is directly taken from the result instance title : string or None if None, then a default title is used. gleft : list of tuples elements for the left table, tuples are (name, value) pairs If gleft is None, then a de

statsmodels.tools.tools.monotone_fn_inverter statsmodels.tools.tools.monotone_fn_inverter(fn, x, vectorized=True, **keywords) Given a monotone function x (no checking is done to verify monotonicity) and a set of x values, return an linearly interpolated approximation to its inverse from its values on x.

statsmodels.sandbox.regression.gmm.IVGMMResults.tvalues static IVGMMResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.tsa.vector_ar.var_model.VARResults.reorder VARResults.reorder(order) [source] Reorder variables for structural specification

statsmodels.regression.quantile_regression.QuantReg class statsmodels.regression.quantile_regression.QuantReg(endog, exog, **kwargs) [source] Quantile Regression Estimate a quantile regression model using iterative reweighted least squares. Parameters: endog : array or dataframe endogenous/response variable exog : array or dataframe exogenous/explanatory variable(s) Notes The Least Absolute Deviation (LAD) estimator is a special case where quantile is set to 0.5 (q argument of the fit

statsmodels.stats.weightstats._zstat_generic2 statsmodels.stats.weightstats._zstat_generic2(value, std_diff, alternative) [source] generic (normal) z-test to save typing can be used as ztest based on summary statistics