statsmodels.emplike.descriptive.DescStatUV.test_joint_skew_kurt DescStatUV.test_joint_skew_kurt(skew0, kurt0, return_weights=False) [source] Returns - 2 x log-likelihood and the p-value for the joint hypothesis test for skewness and kurtosis Parameters: skew0 : float Skewness value to be tested kurt0 : float Kurtosis value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns: test_results : tuple

statsmodels.miscmodels.count.PoissonGMLE.predict PoissonGMLE.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.tsa.arima_model.ARMAResults.predict ARMAResults.predict(start=None, end=None, exog=None, dynamic=False) [source] ARMA model in-sample and out-of-sample prediction Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first forecast is start.

statsmodels.regression.linear_model.WLS.initialize WLS.initialize()

statsmodels.robust.scale.hubers_scale statsmodels.robust.scale.hubers_scale = Huber?s scaling for fitting robust linear models. Huber?s scale is intended to be used as the scale estimate in the IRLS algorithm and is slightly different than the Huber class. Parameters: d : float, optional d is the tuning constant for Huber?s scale. Default is 2.5 tol : float, optional The convergence tolerance maxiter : int, optiona The maximum number of iterations. The default is 30. Notes Huber?s s

statsmodels.tools.eval_measures.maxabs statsmodels.tools.eval_measures.maxabs(x1, x2, axis=0) [source] maximum absolute error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: maxabs : ndarray or float maximum absolute difference along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarray to conver

statsmodels.genmod.families.family.Family.deviance Family.deviance(endog, mu, scale=1.0) [source] Deviance of (endog,mu) pair. Deviance is usually defined as twice the loglikelihood ratio. Parameters: endog : array-like The endogenous response variable mu : array-like The inverse of the link function at the linear predicted values. scale : float, optional An optional scale argument Returns: Deviance : array The value of deviance function defined below. Notes Deviance is defined

statsmodels.stats.diagnostic.CompareCox class statsmodels.stats.diagnostic.CompareCox Cox Test for non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool Formulas from Greene, section 8.3.4 translated to code : produces correct results for Example 8.3, Greene : Methods run(results_x, results_z[, attach]) run Cox test for non-nested models

statsmodels.stats.gof.gof_binning_discrete statsmodels.stats.gof.gof_binning_discrete(rvs, distfn, arg, nsupp=20) [source] get bins for chisquare type gof tests for a discrete distribution Parameters: rvs : array sample data distname : string name of distribution function arg : sequence parameters of distribution nsupp : integer number of bins. The algorithm tries to find bins with equal weights. depending on the distribution, the actual number of bins can be smaller. Returns: fre

statsmodels.stats.weightstats.DescrStatsW.ztest_mean DescrStatsW.ztest_mean(value=0, alternative='two-sided') [source] z-test of Null hypothesis that mean is equal to value. The alternative hypothesis H1 is defined by the following ?two-sided?: H1: mean not equal to value ?larger? : H1: mean larger than value ?smaller? : H1: mean smaller than value Parameters: value : float or array the hypothesized value for the mean alternative : string The alternative hypothesis, H1, has to be one of