statsmodels.tsa.ar_model.ARResults.bic static ARResults.bic() [source]

statsmodels.sandbox.regression.gmm.LinearIVGMM.score_cu LinearIVGMM.score_cu(params, epsilon=None, centered=True)

statsmodels.tsa.vector_ar.var_model.VARResults.resid static VARResults.resid() [source] Residuals of response variable resulting from estimated coefficients

statsmodels.miscmodels.count.PoissonZiGMLE.loglike PoissonZiGMLE.loglike(params)

statsmodels.sandbox.regression.gmm.IVGMM.score_cu IVGMM.score_cu(params, epsilon=None, centered=True)

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.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.sandbox.distributions.transformed.LogTransf_gen.nnlf LogTransf_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.sandbox.distributions.extras.ACSkewT_gen.logpdf ACSkewT_gen.logpdf(x, *args, **kwds) Log of the probability density function at x of the given RV. This uses a more numerically accurate calculation if available. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional sca

statsmodels.genmod.families.family.Gaussian.deviance Gaussian.deviance(endog, mu, scale=1.0) [source] Gaussian deviance function Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns: deviance : float The deviance function at (endog,mu) as defined below. Notes deviance = sum((endog-mu)**2)