statsmodels.discrete.discrete_model.CountResults.f_test CountResults.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 ca

statsmodels.iolib.table.SimpleTable.as_html SimpleTable.as_html(**fmt_dict) [source] Return string. This is the default formatter for HTML tables. An HTML table formatter must accept as arguments a table and a format dictionary.

statsmodels.miscmodels.count.PoissonGMLE.jac PoissonGMLE.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.tsa.arima_model.ARIMA.loglike ARIMA.loglike(params, set_sigma2=True) Compute the log-likelihood for ARMA(p,q) model Notes Likelihood used depends on the method set in fit

statsmodels.tsa.arima_model.ARMA.information ARMA.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.genmod.families.family.Family.weights Family.weights(mu) [source] Weights for IRLS steps Parameters: mu : array-like The transformed mean response variable in the exponential family Returns: w : array The weights for the IRLS steps Notes w = 1 / (link?(mu)**2 * variance(mu))

statsmodels.regression.linear_model.RegressionResults.resid_pearson static RegressionResults.resid_pearson() [source] Residuals, normalized to have unit variance. Returns: An array wresid/sqrt(scale) :

statsmodels.sandbox.regression.gmm.LinearIVGMM.score LinearIVGMM.score(params, weights, **kwds) [source]

statsmodels.miscmodels.count.PoissonOffsetGMLE.hessian PoissonOffsetGMLE.hessian(params) Hessian of log-likelihood evaluated at params

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