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.families.links.Link.deriv2 Link.deriv2(p) [source] Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.tsa.arima_model.ARMAResults.fittedvalues static ARMAResults.fittedvalues() [source]

statsmodels.nonparametric.kernel_density.KDEMultivariateConditional class statsmodels.nonparametric.kernel_density.KDEMultivariateConditional(endog, exog, dep_type, indep_type, bw, defaults=) [source] Conditional multivariate kernel density estimator. Calculates P(Y_1,Y_2,...Y_n | X_1,X_2...X_m) = P(X_1, X_2,...X_n, Y_1, Y_2,..., Y_m)/P(X_1, X_2,..., X_m). The conditional density is by definition the ratio of the two densities, see [R8]. Parameters: endog: list of ndarrays or 2-D ndarray :

statsmodels.regression.quantile_regression.QuantRegResults.wresid static QuantRegResults.wresid()

statsmodels.sandbox.sysreg.SUR class statsmodels.sandbox.sysreg.SUR(sys, sigma=None, dfk=None) [source] Seemingly Unrelated Regression Parameters: sys : list [endog1, exog1, endog2, exog2,...] It will be of length 2 x M, where M is the number of equations endog = exog. sigma : array-like M x M array where sigma[i,j] is the covariance between equation i and j dfk : None, ?dfk1?, or ?dfk2? Default is None. Correction for the degrees of freedom should be specified for small samples. See t

statsmodels.tsa.arima_model.ARMA.hessian ARMA.hessian(params) [source] Compute the Hessian at params, Notes This is a numerical approximation.

statsmodels.genmod.generalized_estimating_equations.GEEResults.split_resid static GEEResults.split_resid() Returns the residuals, the endogeneous data minus the fitted values from the model. The residuals are returned as a list of arrays containing the residuals for each cluster.

statsmodels.regression.quantile_regression.QuantRegResults.centered_tss static QuantRegResults.centered_tss() [source]

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