statsmodels.sandbox.regression.gmm.IVRegressionResults.eigenvals static IVRegressionResults.eigenvals() Return eigenvalues sorted in decreasing order.

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

statsmodels.sandbox.distributions.transformed.ExpTransf_gen class statsmodels.sandbox.distributions.transformed.ExpTransf_gen(kls, *args, **kwargs) [source] Distribution based on log/exp transformation the constructor can be called with a distribution class and generates the distribution of the transformed random variable Methods cdf(x, *args, **kwds) Cumulative distribution function of the given RV. entropy(*args, **kwds) Differential entropy of the RV. est_loc_scale(*args, **kwds) est_

statsmodels.genmod.families.links.identity.deriv2 identity.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.regression.linear_model.OLSResults.cov_params OLSResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like, opti

statsmodels.regression.linear_model.OLSResults.rsquared_adj static OLSResults.rsquared_adj()

statsmodels.sandbox.regression.gmm.LinearIVGMM.predict LinearIVGMM.predict(params, exog=None) [source]

statsmodels.stats.weightstats.DescrStatsW.sum_weights static DescrStatsW.sum_weights() [source]

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

statsmodels.sandbox.regression.gmm.IVGMM.fit IVGMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None) Estimate parameters using GMM and return GMMResults TODO: weight and covariance arguments still need to be made consistent with similar options in other models, see RegressionResult.get_robustcov_results Parameters: start_params : array (optional) starting value for parameters ub minimization.