statsmodels.tsa.ar_model.ARResults.bic static ARResults.bic() [source]

statsmodels.sandbox.regression.gmm.LinearIVGMM.score_cu LinearIVGMM.score_cu(params, epsilon=None, centered=True)

statsmodels.regression.linear_model.RegressionResults.resid_pearson static RegressionResults.resid_pearson() [source] Residuals, normalized to have unit variance. Returns: An array wresid/sqrt(scale) :

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

statsmodels.tsa.vector_ar.var_model.VARResults.sample_acorr VARResults.sample_acorr(nlags=1) [source]

statsmodels.tsa.arima_model.ARIMA.fit ARIMA.fit(start_params=None, trend='c', method='css-mle', transparams=True, solver='lbfgs', maxiter=50, full_output=1, disp=5, callback=None, **kwargs) [source] Fits ARIMA(p,d,q) model by exact maximum likelihood via Kalman filter. Parameters: start_params : array-like, optional Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. See there for more information. transparams : bool, optional Whehter or not to tra

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.get_error NonlinearIVGMM.get_error(params)

statsmodels.iolib.table.SimpleTable.sort SimpleTable.sort() L.sort(cmp=None, key=None, reverse=False) ? stable sort IN PLACE; cmp(x, y) -> -1, 0, 1

statsmodels.sandbox.distributions.extras.ACSkewT_gen.logcdf ACSkewT_gen.logcdf(x, *args, **kwds) Log of the cumulative distribution function 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: logcdf : array_like Lo

statsmodels.sandbox.regression.gmm.LinearIVGMM.fitstart LinearIVGMM.fitstart()