statsmodels.genmod.families.links.probit.deriv2 probit.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

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

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

Regression with Discrete Dependent Variable Regression models for limited and qualitative dependent variables. The module currently allows the estimation of models with binary (Logit, Probit), nominal (MNLogit), or count (Poisson) data. See Module Reference for commands and arguments. Examples # Load the data from Spector and Mazzeo (1980) spector_data = sm.datasets.spector.load() spector_data.exog = sm.add_constant(spector_data.exog) # Logit Model logit_mod = sm.Logit(spector_data.endog, spe

statsmodels.robust.robust_linear_model.RLM.deviance RLM.deviance(tmp_results) [source] Returns the (unnormalized) log-likelihood from the M estimator.

statsmodels.regression.quantile_regression.QuantRegResults.cov_HC3 static QuantRegResults.cov_HC3() See statsmodels.RegressionResults

statsmodels.regression.linear_model.WLS.hessian WLS.hessian(params) The Hessian matrix of the model