statsmodels.sandbox.regression.gmm.LinearIVGMM.score_cu LinearIVGMM.score_cu(params, epsilon=None, centered=True)

statsmodels.tsa.vector_ar.var_model.VARResults.resid static VARResults.resid() [source] Residuals of response variable resulting from estimated coefficients

statsmodels.sandbox.regression.gmm.IVGMM.score_cu IVGMM.score_cu(params, epsilon=None, centered=True)

statsmodels.sandbox.distributions.transformed.LogTransf_gen.fit LogTransf_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.miscmodels.count.PoissonZiGMLE.loglike PoissonZiGMLE.loglike(params)

statsmodels.miscmodels.count.PoissonGMLE.jac PoissonGMLE.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.sandbox.regression.gmm.GMM.calc_weightmatrix GMM.calc_weightmatrix(moms, weights_method='cov', wargs=(), params=None) [source] calculate omega or the weighting matrix Parameters: moms : array, (nobs, nmoms) moment conditions for all observations evaluated at a parameter value weights_method : string ?cov? If method=?cov? is cov then the matrix is calculated as simple covariance of the moment conditions. see fit method for available aoptions for the weight and covariance matri

statsmodels.iolib.table.SimpleTable.as_html SimpleTable.as_html(**fmt_dict) [source] Return string. This is the default formatter for HTML tables. An HTML table formatter must accept as arguments a table and a format dictionary.

statsmodels.sandbox.distributions.transformed.LogTransf_gen.nnlf LogTransf_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.discrete.discrete_model.CountResults.resid static CountResults.resid() [source] Residuals Notes The residuals for Count models are defined as where . Any exposure and offset variables are also handled.