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)

statsmodels.genmod.cov_struct.CovStruct class statsmodels.genmod.cov_struct.CovStruct(cov_nearest_method='clipped') [source] A base class for correlation and covariance structures of grouped data. Each implementation of this class takes the residuals from a regression model that has been fitted to grouped data, and uses them to estimate the within-group dependence structure of the random errors in the model. The state of the covariance structure is represented through the value of the class

statsmodels.tools.tools.categorical statsmodels.tools.tools.categorical(data, col=None, dictnames=False, drop=False) [source] Returns a dummy matrix given an array of categorical variables. Parameters: data : array A structured array, recarray, or array. This can be either a 1d vector of the categorical variable or a 2d array with the column specifying the categorical variable specified by the col argument. col : ?string?, int, or None If data is a structured array or a recarray, col can

statsmodels.sandbox.regression.gmm.IVRegressionResults.uncentered_tss static IVRegressionResults.uncentered_tss()

statsmodels.tsa.arima_model.ARIMA.loglike_css ARIMA.loglike_css(params, set_sigma2=True) Conditional Sum of Squares likelihood function.

statsmodels.regression.linear_model.RegressionResults.pvalues static RegressionResults.pvalues()

statsmodels.sandbox.regression.gmm.LinearIVGMM.momcond_mean LinearIVGMM.momcond_mean(params) mean of moment conditions,