statsmodels.stats.outliers_influence.OLSInfluence.sigma2_not_obsi static OLSInfluence.sigma2_not_obsi() [source] (cached attribute) error variance for all LOOO regressions This is ?mse_resid? from each auxiliary regression. uses results from leave-one-observation-out loop

statsmodels.stats.outliers_influence.OLSInfluence.resid_var static OLSInfluence.resid_var() [source] (cached attribute) estimate of variance of the residuals sigma2 = sigma2_OLS * (1 - hii) where hii is the diagonal of the hat matrix

statsmodels.stats.outliers_influence.OLSInfluence.resid_studentized_internal static OLSInfluence.resid_studentized_internal() [source] (cached attribute) studentized residuals using variance from OLS this uses sigma from original estimate does not require leave one out loop

statsmodels.stats.outliers_influence.OLSInfluence.resid_studentized_external static OLSInfluence.resid_studentized_external() [source] (cached attribute) studentized residuals using LOOO variance this uses sigma from leave-one-out estimates requires leave one out loop for observations

statsmodels.stats.outliers_influence.OLSInfluence.resid_std static OLSInfluence.resid_std() [source] (cached attribute) estimate of standard deviation of the residuals See also resid_var

statsmodels.stats.outliers_influence.OLSInfluence.resid_press static OLSInfluence.resid_press() [source] (cached attribute) PRESS residuals

statsmodels.stats.outliers_influence.OLSInfluence.params_not_obsi static OLSInfluence.params_not_obsi() [source] (cached attribute) parameter estimates for all LOOO regressions uses results from leave-one-observation-out loop

statsmodels.stats.outliers_influence.OLSInfluence.influence static OLSInfluence.influence() [source] (cached attribute) influence measure matches the influence measure that gretl reports u * h / (1 - h) where u are the residuals and h is the diagonal of the hat_matrix

statsmodels.stats.outliers_influence.OLSInfluence.hat_matrix_diag static OLSInfluence.hat_matrix_diag() [source] (cached attribute) diagonal of the hat_matrix for OLS Notes temporarily calculated here, this should go to model class

statsmodels.stats.outliers_influence.OLSInfluence.hat_diag_factor static OLSInfluence.hat_diag_factor() [source] (cached attribute) factor of diagonal of hat_matrix used in influence this might be useful for internal reuse h / (1 - h)