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

Sandbox This sandbox contains code that is for various resons not ready to be included in statsmodels proper. It contains modules from the old stats.models code that have not been tested, verified and updated to the new statsmodels structure: cox survival model, mixed effects model with repeated measures, generalized additive model and the formula framework. The sandbox also contains code that is currently being worked on until it fits the pattern of statsmodels or is sufficiently tested. All s

statsmodels.tsa.arima_model.ARIMAResults.pvalues static ARIMAResults.pvalues()

statsmodels.stats.weightstats.DescrStatsW.var static DescrStatsW.var() [source] variance with default degrees of freedom correction

statsmodels.discrete.discrete_model.BinaryResults.wald_test BinaryResults.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. tuple : A

statsmodels.tsa.vector_ar.irf.IRAnalysis.fevd_table IRAnalysis.fevd_table() [source]

statsmodels.tsa.vector_ar.irf.IRAnalysis.cov IRAnalysis.cov(orth=False) [source] Compute asymptotic standard errors for impulse response coefficients Notes Lutkepohl eq 3.7.5