statsmodels.robust.norms.estimate_location statsmodels.robust.norms.estimate_location(a, scale, norm=None, axis=0, initial=None, maxiter=30, tol=1e-06) [source] M-estimator of location using self.norm and a current estimator of scale. This iteratively finds a solution to norm.psi((a-mu)/scale).sum() == 0 Parameters: a : array Array over which the location parameter is to be estimated scale : array Scale parameter to be used in M-estimator norm : RobustNorm, optional Robust norm used in

statsmodels.regression.quantile_regression.QuantReg.information QuantReg.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.duration.hazard_regression.PHRegResults.summary PHRegResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the proportional hazards regression results. Parameters: yname : string, optional Default is y xname : list of strings, optional Default is x# for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence interval

statsmodels.genmod.generalized_estimating_equations.GEEResults.tvalues static GEEResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.stats.power.GofChisquarePower.solve_power GofChisquarePower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, n_bins=2) [source] solve for any one parameter of the power of a one sample chisquare-test for the one sample chisquare-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. n_bins needs to be defined, a default=2 is used. Parameters: effect_size : float standardized effect size, according to

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.equations static DynamicVAR.equations() [source]

statsmodels.regression.linear_model.RegressionResults.scale static RegressionResults.scale() [source]

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.pdf TransfTwo_gen.pdf(x, *args, **kwds) Probability density 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: pdf : ndarray Probability density

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.var TransfTwo_gen.var(*args, **kwds) Variance of the distribution Parameters: 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: var : float the variance of the distribution

statsmodels.tsa.ar_model.ARResults.resid static ARResults.resid() [source]