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

statsmodels.discrete.discrete_model.BinaryModel.initialize BinaryModel.initialize() Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

statsmodels.iolib.smpickle.load_pickle statsmodels.iolib.smpickle.load_pickle(fname) [source] Load a previously saved object from file Parameters: fname : str Filename to unpickle Notes This method can be used to load both models and results.

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.entropy SkewNorm2_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.discrete.discrete_model.DiscreteModel.initialize DiscreteModel.initialize() [source] Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

statsmodels.sandbox.regression.gmm.GMMResults.compare_j GMMResults.compare_j(other) [source] overidentification test for comparing two nested gmm estimates This assumes that some moment restrictions have been dropped in one of the GMM estimates relative to the other. Not tested yet We are comparing two separately estimated models, that use different weighting matrices. It is not guaranteed that the resulting difference is positive. TODO: Check in which cases Stata programs use the same weigt

statsmodels.miscmodels.count.PoissonOffsetGMLE.initialize PoissonOffsetGMLE.initialize()

statsmodels.sandbox.distributions.transformed.LogTransf_gen.logpdf LogTransf_gen.logpdf(x, *args, **kwds) Log of the probability density function at x of the given RV. This uses a more numerically accurate calculation if available. Parameters: x : array_like quantiles 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, opti

statsmodels.regression.linear_model.OLSResults.wresid static OLSResults.wresid()

statsmodels.tsa.arima_model.ARIMA.information ARIMA.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.