statsmodels.robust.scale.stand_mad statsmodels.robust.scale.stand_mad(a, c=0.67448975019608171, axis=0) [source]

statsmodels.stats.weightstats.CompareMeans.ztest_ind CompareMeans.ztest_ind(alternative='two-sided', usevar='pooled', value=0) [source] z-test for the null hypothesis of identical means Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The alternative hypothesis, H1, has to be one of the following ?two-sided?: H1: difference in means not equal to value (default) ?larger? : H1: difference in means larger than value ?smaller? :

statsmodels.sandbox.distributions.transformed.Transf_gen.moment Transf_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.discrete.discrete_model.ProbitResults.aic static ProbitResults.aic()

statsmodels.sandbox.regression.gmm.IV2SLS.initialize IV2SLS.initialize() [source]

statsmodels.sandbox.regression.gmm.GMMResults.q static GMMResults.q() [source]

statsmodels.regression.linear_model.OLSResults.initialize OLSResults.initialize(model, params, **kwd)

statsmodels.sandbox.regression.gmm.IVRegressionResults.HC1_se static IVRegressionResults.HC1_se() See statsmodels.RegressionResults

statsmodels.sandbox.distributions.extras.ACSkewT_gen.expect ACSkewT_gen.expect(func=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Calculate expected value of a function with respect to the distribution. The expected value of a function f(x) with respect to a distribution dist is defined as: ubound E[x] = Integral(f(x) * dist.pdf(x)) lbound Parameters: func : callable, optional Function for which integral is calculated. Takes only one argument.

statsmodels.tsa.arima_model.ARMAResults.tvalues static ARMAResults.tvalues() Return the t-statistic for a given parameter estimate.