static DynamicVAR.resid()

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.resid static DynamicVAR.resid() [source]

NormExpan_gen.est_loc_scale()

statsmodels.sandbox.distributions.extras.NormExpan_gen.est_loc_scale NormExpan_gen.est_loc_scale(*args, **kwds) est_loc_scale is deprecated! This function is deprecated, use self.fit_loc_scale(data) instead.

static RegressionResults.bic()

statsmodels.regression.linear_model.RegressionResults.bic static RegressionResults.bic() [source]

DiscreteResults.t_test()

statsmodels.discrete.discrete_model.DiscreteResults.t_test DiscreteResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tup

NegativeBinomialResults.normalized_cov_params()

statsmodels.discrete.discrete_model.NegativeBinomialResults.normalized_cov_params NegativeBinomialResults.normalized_cov_params()

static CountResults.llf()

statsmodels.discrete.discrete_model.CountResults.llf static CountResults.llf()

stats.weightstats._tstat_generic()

statsmodels.stats.weightstats._tstat_generic statsmodels.stats.weightstats._tstat_generic(value1, value2, std_diff, dof, alternative, diff=0) [source] generic ttest to save typing

NormExpan_gen.entropy()

statsmodels.sandbox.distributions.extras.NormExpan_gen.entropy NormExpan_gen.entropy(*args, **kwds) Differential entropy of the RV. Parameters: 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).

IRAnalysis.lr_effect_stderr()

statsmodels.tsa.vector_ar.irf.IRAnalysis.lr_effect_stderr IRAnalysis.lr_effect_stderr(orth=False) [source]

MultinomialModel.pdf()

statsmodels.discrete.discrete_model.MultinomialModel.pdf MultinomialModel.pdf(X) The probability density (mass) function of the model.