statsmodels.stats.moment_helpers.mvsk2mc statsmodels.stats.moment_helpers.mvsk2mc(args) [source] convert mean, variance, skew, kurtosis to central moments

statsmodels.genmod.families.links.CDFLink.inverse CDFLink.inverse(z) [source] The inverse of the CDF link Parameters: z : array-like The value of the inverse of the link function at p Returns: p : array Mean probabilities. The value of the inverse of CDF link of z Notes g^(-1)(z) = dbn.cdf(z)

statsmodels.robust.robust_linear_model.RLM.score RLM.score(params) [source]

statsmodels.sandbox.tsa.fftarma.ArmaFft.plot4 ArmaFft.plot4(fig=None, nobs=100, nacf=20, nfreq=100) [source]

statsmodels.discrete.discrete_model.NegativeBinomial.predict NegativeBinomial.predict(params, exog=None, exposure=None, offset=None, linear=False) Predict response variable of a count model given exogenous variables. Notes If exposure is specified, then it will be logged by the method. The user does not need to log it first.

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

statsmodels.discrete.discrete_model.Probit.fit_regularized Probit.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) 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 the solution for t

statsmodels.iolib.summary2.Summary.as_latex Summary.as_latex() [source] Generate LaTeX Summary Table

statsmodels.sandbox.regression.gmm.GMMResults class statsmodels.sandbox.regression.gmm.GMMResults(*args, **kwds) [source] just a storage class right now Methods calc_cov_params(moms, gradmoms[, weights, ...]) calculate covariance of parameter estimates compare_j(other) overidentification test for comparing two nested gmm estimates conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters. cov_params(**kwds) f_test(r_matrix[, cov_p, scale, invcov]) Compute

statsmodels.sandbox.distributions.transformed.TransfTwo_gen class statsmodels.sandbox.distributions.transformed.TransfTwo_gen(kls, func, funcinvplus, funcinvminus, derivplus, derivminus, *args, **kwargs) [source] Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s