statsmodels.discrete.discrete_model.DiscreteResults.wald_test DiscreteResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. 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

statsmodels.tsa.vector_ar.var_model.VARResults.acf VARResults.acf(nlags=None) Compute theoretical autocovariance function Returns: acf : ndarray (p x k x k)

statsmodels.genmod.families.links.Log.deriv Log.deriv(p) [source] Derivative of log transform link function Parameters: p : array-like Mean parameters Returns: g?(p) : array derivative of log transform of x Notes g(x) = 1/x

statsmodels.discrete.discrete_model.NegativeBinomialResults.fittedvalues static NegativeBinomialResults.fittedvalues()

statsmodels.genmod.generalized_linear_model.GLMResults.deviance static GLMResults.deviance() [source]

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

statsmodels.miscmodels.count.PoissonOffsetGMLE.loglike PoissonOffsetGMLE.loglike(params)

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

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

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|