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

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|

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

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

statsmodels.sandbox.stats.multicomp.StepDown class statsmodels.sandbox.stats.multicomp.StepDown(vals, nobs_all, var_all, df=None) [source] a class for step down methods This is currently for simple tree subset descend, similar to homogeneous_subsets, but checks all leave-one-out subsets instead of assuming an ordered set. Comment in SAS manual: SAS only uses interval subsets of the sorted list, which is sufficient for range tests (maybe also equal variance and balanced sample sizes are requi

statsmodels.tsa.filters.cf_filter.cffilter statsmodels.tsa.filters.cf_filter.cffilter(X, low=6, high=32, drift=True) [source] Christiano Fitzgerald asymmetric, random walk filter Parameters: X : array-like 1 or 2d array to filter. If 2d, variables are assumed to be in columns. low : float Minimum period of oscillations. Features below low periodicity are filtered out. Default is 6 for quarterly data, giving a 1.5 year periodicity. high : float Maximum period of oscillations. Features a