statsmodels.sandbox.regression.gmm.IV2SLS.information IV2SLS.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.regression.try_catdata.groupsstats_dummy statsmodels.sandbox.regression.try_catdata.groupsstats_dummy(y, x, nonseq=0) [source]

statsmodels.discrete.discrete_model.MultinomialResults.margeff MultinomialResults.margeff() [source]

statsmodels.tsa.vector_ar.var_model.VARResults.mean VARResults.mean() Mean of stable process Lutkepohl eq. 2.1.23

statsmodels.robust.robust_linear_model.RLMResults.chisq static RLMResults.chisq() [source]

statsmodels.miscmodels.tmodel.TLinearModel class statsmodels.miscmodels.tmodel.TLinearModel(endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=None, **kwds) [source] Maximum Likelihood Estimation of Linear Model with t-distributed errors This is an example for generic MLE. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiati

statsmodels.sandbox.distributions.extras.ACSkewT_gen.std ACSkewT_gen.std(*args, **kwds) Standard deviation of the distribution. 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) Returns: std : float standard deviation of the distribution

statsmodels.miscmodels.tmodel.TLinearModel.from_formula classmethod TLinearModel.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame ar

statsmodels.regression.linear_model.OLS.initialize OLS.initialize()

statsmodels.miscmodels.count.PoissonOffsetGMLE.fit PoissonOffsetGMLE.fit(start_params=None, method='nm', maxiter=500, full_output=1, disp=1, callback=None, retall=0, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.LikelihoodModel.fit