statsmodels.genmod.families.family.NegativeBinomial class statsmodels.genmod.families.family.NegativeBinomial(link=, alpha=1.0) [source] Negative Binomial exponential family. Parameters: link : a link instance, optional The default link for the negative binomial family is the log link. Available links are log, cloglog, identity, nbinom and power. See statsmodels.family.links for more information. alpha : float, optional The ancillary parameter for the negative binomial distribution. For

statsmodels.genmod.families.links.nbinom.inverse nbinom.inverse(z) Inverse of the negative binomial transform Parameters: z : array-like The value of the inverse of the negative binomial link at p. Returns : ??- : p : array Mean parameters Notes g^(-1)(z) = exp(z)/(alpha*(1-exp(z)))

statsmodels.tsa.ar_model.AR.fit AR.fit(maxlag=None, method='cmle', ic=None, trend='c', transparams=True, start_params=None, solver='lbfgs', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the unconditional maximum likelihood of an AR(p) process. Parameters: maxlag : int If ic is None, then maxlag is the lag length used in fit. If ic is specified then maxlag is the highest lag order used to select the correct lag order. If maxlag is None, the default is round(12*(no

statsmodels.genmod.generalized_estimating_equations.GEE.estimate_scale GEE.estimate_scale() [source] Returns an estimate of the scale parameter phi at the current parameter value.

statsmodels.discrete.discrete_model.Logit.information Logit.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.sysreg.SUR.initialize SUR.initialize() [source]

statsmodels.sandbox.regression.gmm.IV2SLS.score IV2SLS.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.discrete.discrete_model.MultinomialModel.fit_regularized MultinomialModel.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) [source] Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, optional Initia

statsmodels.sandbox.distributions.transformed.lognormalg statsmodels.sandbox.distributions.transformed.lognormalg = a class for non-linear monotonic transformation of a continuous random variable

statsmodels.regression.quantile_regression.QuantRegResults.pvalues static QuantRegResults.pvalues()