statsmodels.sandbox.distributions.transformed.ExpTransf_gen.freeze ExpTransf_gen.freeze(*args, **kwds) Freeze the distribution for the given arguments. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution. Should include all the non-optional arguments, may include loc and scale. Returns: rv_frozen : rv_frozen instance The frozen distribution.

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.fit_loc_scale ExpTransf_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.fit ExpTransf_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape param

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.expect ExpTransf_gen.expect(func=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Calculate expected value of a function with respect to the distribution. The expected value of a function f(x) with respect to a distribution dist is defined as: ubound E[x] = Integral(f(x) * dist.pdf(x)) lbound Parameters: func : callable, optional Function for which integral is calculated. Takes only one

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

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.entropy ExpTransf_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.ExpTransf_gen.cdf ExpTransf_gen.cdf(x, *args, **kwds) Cumulative distribution function of the given RV. 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, optional scale parameter (default=1) Returns: cdf : ndarray Cumulative distributi

statsmodels.genmod.cov_struct.Exchangeable.update Exchangeable.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.genmod.cov_struct.Exchangeable.summary Exchangeable.summary() [source]

statsmodels.genmod.cov_struct.Exchangeable.initialize Exchangeable.initialize(model) Called by GEE, used by implementations that need additional setup prior to running fit. Parameters: model : GEE class A reference to the parent GEE class instance.