statsmodels.robust.norms.HuberT.psi_deriv HuberT.psi_deriv(z) [source] The derivative of Huber?s t psi function Notes Used to estimate the robust covariance matrix.

Installation Using setuptools To obtain the latest released version of statsmodels using setuptools: easy_install -U statsmodels Or follow this link to our PyPI page. Obtaining the Source We do not release very often but the master branch of our source code is usually fine for everyday use. You can get the latest source from our github repository. Or if you have git installed: git clone git://github.com/statsmodels/statsmodels.git If you want to keep up to date with the source on github ju

statsmodels.regression.linear_model.RegressionResults.cov_HC2 static RegressionResults.cov_HC2() [source] See statsmodels.RegressionResults

statsmodels.discrete.discrete_model.Probit.fit Probit.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str, optional Th

statsmodels.sandbox.distributions.transformed.SquareFunc.inverseminus SquareFunc.inverseminus(x) [source]

statsmodels.sandbox.regression.gmm.IVGMMResults.conf_int IVGMMResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance 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 method : string Not Implemented Yet Method to estimate the confidence_interval.

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.nnlf ExpTransf_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

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

statsmodels.sandbox.regression.gmm.GMM.score_cu GMM.score_cu(params, epsilon=None, centered=True) [source]

statsmodels.duration.hazard_regression.PHRegResults.f_test PHRegResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test