statsmodels.sandbox.regression.gmm.LinearIVGMM.fitstart LinearIVGMM.fitstart()

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

statsmodels.discrete.discrete_model.BinaryModel.score BinaryModel.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.sandbox.distributions.transformed.LogTransf_gen.est_loc_scale LogTransf_gen.est_loc_scale(*args, **kwds) est_loc_scale is deprecated! This function is deprecated, use self.fit_loc_scale(data) instead.

statsmodels.sandbox.regression.gmm.IVRegressionResults.conf_int IVRegressionResults.conf_int(alpha=0.05, cols=None) Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return Notes The confidence interval is based on Student?s t-distribution.

statsmodels.regression.linear_model.OLSResults.cov_HC1 static OLSResults.cov_HC1() See statsmodels.RegressionResults

statsmodels.sandbox.regression.gmm.IVGMMResults.ssr static IVGMMResults.ssr() [source]

statsmodels.tsa.vector_ar.var_model.VARResults.plot_sample_acorr VARResults.plot_sample_acorr(nlags=10, linewidth=8) [source] Plot theoretical autocorrelation function

statsmodels.sandbox.distributions.extras.ACSkewT_gen.logcdf ACSkewT_gen.logcdf(x, *args, **kwds) Log of the cumulative distribution function at x of the given RV. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: logcdf : array_like Lo

statsmodels.iolib.table.SimpleTable.sort SimpleTable.sort() L.sort(cmp=None, key=None, reverse=False) ? stable sort IN PLACE; cmp(x, y) -> -1, 0, 1