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.tsa.ar_model.ARResults.fittedvalues static ARResults.fittedvalues() [source]

statsmodels.miscmodels.count.PoissonGMLE.reduceparams PoissonGMLE.reduceparams(params)

statsmodels.miscmodels.count.PoissonGMLE.score PoissonGMLE.score(params) Gradient of log-likelihood evaluated at params

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

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

statsmodels.tsa.arima_model.ARMAResults.hqic static ARMAResults.hqic() [source]

statsmodels.stats.proportion.binom_tost statsmodels.stats.proportion.binom_tost(count, nobs, low, upp) [source] exact TOST test for one proportion using binomial distribution Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. low, upp : floats lower and upper limit of equivalence region Returns: pvalue : float p-value of equivalence test pval_low, pval_upp : floats p-values of lower and upper one-

statsmodels.miscmodels.count.PoissonZiGMLE.predict PoissonZiGMLE.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.

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