statsmodels.sandbox.tsa.fftarma.ArmaFft.from_estimation classmethod ArmaFft.from_estimation(model_results, nobs=None) Create ArmaProcess instance from ARMA estimation results Parameters: model_results : ARMAResults instance A fitted model nobs : int, optional If None, nobs is taken from the results

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.sf TransfTwo_gen.sf(x, *args, **kwds) Survival function (1-cdf) at x of the given RV. Parameters: x : array_like quantiles 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: sf : array_like Survival function evalu

statsmodels.iolib.table.SimpleTable class statsmodels.iolib.table.SimpleTable(data, headers=None, stubs=None, title='', datatypes=None, csv_fmt=None, txt_fmt=None, ltx_fmt=None, html_fmt=None, celltype=None, rowtype=None, **fmt_dict) [source] Produce a simple ASCII, CSV, HTML, or LaTeX table from a rectangular (2d!) array of data, not necessarily numerical. Directly supports at most one header row, which should be the length of data[0]. Directly supports at most one stubs column, which must

statsmodels.tsa.vector_ar.var_model.VARResults.sigma_u_mle static VARResults.sigma_u_mle() [source] (Biased) maximum likelihood estimate of noise process covariance

statsmodels.sandbox.distributions.transformed.loggammaexpg statsmodels.sandbox.distributions.transformed.loggammaexpg = univariate distribution of a non-linear monotonic transformation of a random variable

statsmodels.sandbox.distributions.extras.NormExpan_gen.fit_loc_scale NormExpan_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.

statsmodels.discrete.discrete_model.CountModel.loglike CountModel.loglike(params) Log-likelihood of model.

statsmodels.tsa.arima_model.ARIMAResults class statsmodels.tsa.arima_model.ARIMAResults(model, params, normalized_cov_params=None, scale=1.0) [source] Methods aic() arfreq() Returns the frequency of the AR roots. arparams() arroots() bic() bse() conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters. cov_params() f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis. fittedvalues() forecast([steps, exog,

statsmodels.tsa.ar_model.AR.initialize AR.initialize() [source]

statsmodels.miscmodels.count.PoissonZiGMLE.information PoissonZiGMLE.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.