statsmodels.stats.gof.powerdiscrepancy statsmodels.stats.gof.powerdiscrepancy(observed, expected, lambd=0.0, axis=0, ddof=0) [source] Calculates power discrepancy, a class of goodness-of-fit tests as a measure of discrepancy between observed and expected data. This contains several goodness-of-fit tests as special cases, see the describtion of lambd, the exponent of the power discrepancy. The pvalue is based on the asymptotic chi-square distribution of the test statistic. freeman_tukey: D(x|

statsmodels.tsa.vector_ar.irf.IRAnalysis.G static IRAnalysis.G() [source]

statsmodels.genmod.generalized_linear_model.GLMResults.bse static GLMResults.bse()

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

statsmodels.sandbox.distributions.extras.NormExpan_gen.nnlf NormExpan_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.sandbox.distributions.transformed.LogTransf_gen.entropy LogTransf_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).

statsmodels.genmod.cov_struct.Nested.summary Nested.summary() [source] Returns a summary string describing the state of the dependence structure.

statsmodels.sandbox.regression.gmm.GMM.fit GMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None) [source] Estimate parameters using GMM and return GMMResults TODO: weight and covariance arguments still need to be made consistent with similar options in other models, see RegressionResult.get_robustcov_results Parameters: start_params : array (optional) starting value for parameters ub minimiza

statsmodels.regression.quantile_regression.QuantRegResults.resid_pearson static QuantRegResults.resid_pearson() Residuals, normalized to have unit variance. Returns: An array wresid/sqrt(scale) :

statsmodels.nonparametric.kernel_regression.KernelReg class statsmodels.nonparametric.kernel_regression.KernelReg(endog, exog, var_type, reg_type='ll', bw='cv_ls', defaults=) [source] Nonparametric kernel regression class. Calculates the conditional mean E[y|X] where y = g(X) + e. Note that the ?local constant? type of regression provided here is also known as Nadaraya-Watson kernel regression; ?local linear? is an extension of that which suffers less from bias issues at the edge of the supp