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

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

statsmodels.regression.quantile_regression.QuantRegResults.cov_HC1 static QuantRegResults.cov_HC1() See statsmodels.RegressionResults

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

statsmodels.iolib.table.SimpleTable.reverse SimpleTable.reverse() L.reverse() ? reverse IN PLACE

statsmodels.sandbox.regression.gmm.IVRegressionResults.get_robustcov_results IVRegressionResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds) create new results instance with robust covariance as default Parameters: cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. kwds : depends on cov_type Required or optional arguments for ro

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.discrete.discrete_model.BinaryModel.hessian BinaryModel.hessian(params) The Hessian matrix of the model