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

statsmodels.tsa.varma_process.VarmaPoly.hstack VarmaPoly.hstack(a=None, name='ar') [source] stack lagpolynomial horizontally in 2d array

statsmodels.genmod.cov_struct.Autoregressive.update Autoregressive.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.robust.norms.RobustNorm.psi_deriv RobustNorm.psi_deriv(z) [source] Deriative of psi. Used to obtain robust covariance matrix. See statsmodels.rlm for more information. Abstract method: psi_derive = psi?

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

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

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.momcond NonlinearIVGMM.momcond(params)

statsmodels.sandbox.stats.multicomp.GroupsStats.groupsswithin GroupsStats.groupsswithin() [source]