statsmodels.discrete.discrete_model.NegativeBinomial class statsmodels.discrete.discrete_model.NegativeBinomial(endog, exog, loglike_method='nb2', offset=None, exposure=None, missing='none', **kwargs) [source] Negative Binomial Model for count data Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.nnlf TransfTwo_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.regression.gmm.GMMResults.tvalues static GMMResults.tvalues() Return the t-statistic for a given parameter estimate.

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

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

statsmodels.regression.linear_model.GLSAR.fit_regularized GLSAR.fit_regularized(method='coord_descent', maxiter=1000, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-08, zero_tol=1e-08, **kwargs) Return a regularized fit to a linear regression model. Parameters: method : string Only the coordinate descent algorithm is implemented. maxiter : integer The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha : scalar or ar

statsmodels.iolib.table.SimpleTable.insert_stubs SimpleTable.insert_stubs(loc, stubs) [source] Return None. Insert column of stubs at column loc. If there is a header row, it gets an empty cell. So len(stubs) should equal the number of non-header rows.

statsmodels.tsa.vector_ar.var_model.FEVD.cov FEVD.cov() [source] Compute asymptotic standard errors

statsmodels.sandbox.regression.gmm.IVRegressionResults.save IVRegressionResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. No