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

statsmodels.regression.linear_model.RegressionResults.get_robustcov_results RegressionResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds) [source] create new results instance with robust covariance as default Parameters: cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. kwds : depends on cov_type Required or optional arguments

statsmodels.tsa.arima_model.ARMAResults.cov_params ARMAResults.cov_params() [source]

statsmodels.discrete.discrete_model.CountResults.initialize CountResults.initialize(model, params, **kwd)

statsmodels.regression.linear_model.RegressionResults.tvalues static RegressionResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.discrete.discrete_model.NegativeBinomial.hessian NegativeBinomial.hessian(params) The Hessian matrix of the model

statsmodels.miscmodels.tmodel.TLinearModel.initialize TLinearModel.initialize() [source]

endog, exog, what?s that? Statsmodels is using endog and exog as names for the data, the observed variables that are used in an estimation problem. Other names that are often used in different statistical packages or text books are, for example, endog exog y x y variable x variable left hand side (LHS) right hand side (RHS) dependent variable independent variable regressand regressors outcome design response variable explanatory variable The usage is quite often domain and model specific; how

statsmodels.tsa.arima_process.lpol_fima statsmodels.tsa.arima_process.lpol_fima(d, n=20) [source] MA representation of fractional integration Parameters: d : float fractional power n : int number of terms to calculate, including lag zero Returns: ma : array coefficients of lag polynomial