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.sandbox.regression.gmm.GMMResults.tvalues static GMMResults.tvalues() Return the t-statistic for a given parameter estimate.

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.distributions.extras.ACSkewT_gen.moment ACSkewT_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.iolib.table.SimpleTable.append SimpleTable.append() L.append(object) ? append object to end

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.regression.gmm.IVGMM.fit IVGMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None) Estimate parameters using GMM and return GMMResults TODO: weight and covariance arguments still need to be made consistent with similar options in other models, see RegressionResult.get_robustcov_results Parameters: start_params : array (optional) starting value for parameters ub minimization.

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