statsmodels.discrete.discrete_model.NegativeBinomialResults.aic static NegativeBinomialResults.aic() [source]

statsmodels.discrete.discrete_model.CountModel.cov_params_func_l1 CountModel.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.discrete.discrete_model.ProbitResults.pvalues static ProbitResults.pvalues()

statsmodels.discrete.discrete_model.NegativeBinomialResults.prsquared static NegativeBinomialResults.prsquared()

statsmodels.genmod.generalized_linear_model.GLM class statsmodels.genmod.generalized_linear_model.GLM(endog, exog, family=None, offset=None, exposure=None, missing='none', **kwargs) [source] Generalized Linear Models class GLM inherits from statsmodels.base.model.LikelihoodModel Parameters: endog : array-like 1d array of endogenous response variable. This array can be 1d or 2d. Binomial family models accept a 2d array with two columns. If supplied, each observation is expected to be [succe

statsmodels.stats.moment_helpers.mnc2cum statsmodels.stats.moment_helpers.mnc2cum(mnc) [source] convert non-central moments to cumulants recursive formula produces as many cumulants as moments http://en.wikipedia.org/wiki/Cumulant#Cumulants_and_moments

statsmodels.nonparametric.kernel_regression.KernelCensoredReg.sig_test KernelCensoredReg.sig_test(var_pos, nboot=50, nested_res=25, pivot=False) Significance test for the variables in the regression. Parameters: var_pos: sequence : The position of the variable in exog to be tested. Returns: sig: str : The level of significance: * : at 90% confidence level ** : at 95% confidence level *** : at 99* confidence level ?Not Significant? : if not significant

statsmodels.regression.linear_model.WLS.score WLS.score(params) Score vector of model. The gradient of logL with respect to each parameter.

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

statsmodels.stats.diagnostic.unitroot_adf statsmodels.stats.diagnostic.unitroot_adf(x, maxlag=None, trendorder=0, autolag='AIC', store=False)