statsmodels.miscmodels.count.PoissonGMLE.predict_distribution PoissonGMLE.predict_distribution(exog) [source] return frozen scipy.stats distribution with mu at estimated prediction

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.std ExpTransf_gen.std(*args, **kwds) Standard deviation of the distribution. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: std : float standard deviation of the distribution

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

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

statsmodels.genmod.families.family.Gamma.weights Gamma.weights(mu) Weights for IRLS steps Parameters: mu : array-like The transformed mean response variable in the exponential family Returns: w : array The weights for the IRLS steps Notes w = 1 / (link?(mu)**2 * variance(mu))

statsmodels.genmod.families.links.Link.deriv Link.deriv(p) [source] Derivative of the link function g?(p). Just a placeholder. Parameters: p : array-like Returns: The value of the derivative of the link function g?(p) :

statsmodels.discrete.discrete_model.MNLogit.loglike_and_score MNLogit.loglike_and_score(params) [source] Returns log likelihood and score, efficiently reusing calculations. Note that both of these returned quantities will need to be negated before being minimized by the maximum likelihood fitting machinery.

statsmodels.discrete.discrete_model.CountModel.fit_regularized CountModel.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) [source] Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, optional Initial guess of t

statsmodels.sandbox.regression.gmm.LinearIVGMM.fit LinearIVGMM.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 m

statsmodels.nonparametric.kernel_regression.KernelReg.r_squared KernelReg.r_squared() [source] Returns the R-Squared for the nonparametric regression. Notes For more details see p.45 in [2] The R-Squared is calculated by: where is the mean calculated in fit at the exog points.