Quantile DEA

Résumé du livre

Conventional non-parametric linear programming (LP) based DEA models have the advantage of being able to estimate multiple input-output efficiency metrics but suffer from sensitivity to outliers and statistical observational noise. Previous observation-deleting approaches to the outlier/noise problem have been somewhat ad hoc usually requiring iterative LP and non-LP problem solving methods. We present the theory and methodology of quantile-DEA (QDEA), similar in concept to quantile-regression, which enables the analyst to directly use LP to obtain efficiency metrics while specifying that no more than q-percent of data points can lie external to the efficiency hull. Using simulated data, we demonstrate the ability of QDEA to obtain efficiency metrics while addressing outlier bias and statistical noise in observations.