statsmodels.sandbox.regression.gmm.GMMResults.initialize GMMResults.initialize(model, params, **kwd)

statsmodels.regression.linear_model.GLSAR.initialize GLSAR.initialize()

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

statsmodels.robust.norms.RobustNorm.psi RobustNorm.psi(z) [source] Derivative of rho. Sometimes referred to as the influence function. Abstract method: psi = rho?

statsmodels.sandbox.regression.gmm.GMMResults.normalized_cov_params GMMResults.normalized_cov_params()

statsmodels.discrete.discrete_model.Poisson.fit Poisson.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

statsmodels.tsa.ar_model.ARResults.tvalues static ARResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.discrete.discrete_model.MultinomialResults.wald_test MultinomialResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. 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 can be given as a string. See the examples.

statsmodels.iolib.table.SimpleTable.remove SimpleTable.remove() L.remove(value) ? remove first occurrence of value. Raises ValueError if the value is not present.

statsmodels.genmod.generalized_estimating_equations.GEEMargins.tvalues static GEEMargins.tvalues() [source]