15 most important predictors derived from the L1 regularized logistic regression analysis (LASSO) for profit outliers determined by IQR method.

<p>Predictors were ordered by the magnitude of their odds ratio (n = 20,000, training set).</p

Outlier cases defined for IQR definition (n = 28,892).

<p>Outlier cases defined for IQR definition (n = 28,892).</p

Prognostic accuracy of the multivariate regression model and the two variable selection models for the predictors of deficit and profit cases.

<p>Outliers were selected with the IQR method. Results are given as area under the curve (AUC) for a receiver operating characteristic (ROC) curve and the corresponding 95% confidence interval (CI) (n = 8,892, test set).</p

Sample characteristics.

<p>Outliers selected with the IQR method.</p

Quantile regression of ten selected predictors for earnings (n = 20,000, training set).

<p>Notes: the selection of the predictors was based on the judgement of the authors, influenced by the results from the two predictor selection methods (L1 regularized regression and Random forest). Q10, Q20, Q50, Q80 and Q90 denote the 10%, 20%, 50%, 80% and 90% quantiles. The results shown are the obtained regression coefficients and the corresponding p values.</p><p>Quantile regression of ten selected predictors for earnings (n = 20,000, training set).</p

ROC-curves for the multivariate regression model and the two variable selection methods for the prediction of deficit outliers (outlier selection with IQR method) (n = 8,892, test set).

<p>ROC-curves for the multivariate regression model and the two variable selection methods for the prediction of deficit outliers (outlier selection with IQR method) (n = 8,892, test set).</p

15 most important predictors for profit outliers defined by the IQR method derived from Random forest analysis.

<p>Predictors were ordered according to the order of magnitude of their accuracy (n = 20,000, training set).</p