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

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

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

statsmodels.nonparametric.bandwidths.select_bandwidth statsmodels.nonparametric.bandwidths.select_bandwidth(x, bw, kernel) [source] Selects bandwidth for a selection rule bw this is a wrapper around existing bandwidth selection rules Parameters: x : array-like Array for which to get the bandwidth bw : string name of bandwidth selection rule, currently supported are: normal_reference, scott, silverman kernel : not used yet Returns: bw : float The estimate of the bandwidth

statsmodels.sandbox.regression.gmm.GMMResults.cov_params GMMResults.cov_params(**kwds) [source]

statsmodels.genmod.generalized_estimating_equations.GEEMargins.pvalues static GEEMargins.pvalues() [source]

statsmodels.genmod.families.links.identity.inverse identity.inverse(z) Inverse of the power transform link function Parameters: `z` : array-like Value of the transformed mean parameters at p Returns: `p` : array Mean parameters Notes g^(-1)(z`) = z`**(1/`power)

statsmodels.discrete.discrete_model.DiscreteModel.fit_regularized DiscreteModel.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=True, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, qc_verbose=False, **kwargs) [source] Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, o

statsmodels.genmod.generalized_linear_model.GLMResults.initialize GLMResults.initialize(model, params, **kwd)

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.stats ExpTransf_gen.stats(*args, **kwds) Some statistics of the given 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 (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which mo