Generalized theory for measuring efficiency of individuals and groups
提出了一个统一的广义框架，用于聚合Nerlovian利润指标和方向距离函数，该框架比以往方法更灵活，并涵盖了许多其他方法作为特例。
Abstract We present a cohesive generalized framework for an aggregation of the Nerlovian profit indicators and of the directional distance functions, frequently used in productivity and efficiency analysis in operations research and econometrics (e.g., via data envelopment analysis or stochastic frontier analysis). Our theoretical framework allows for greater flexibility than previous approaches, and embraces many other approaches as special cases. In the proposed aggregation scheme, the aggregation weights are mathematically derived from assumptions made about the optimization behavior and about the chosen directions of measurement. We also discuss various interesting special cases of popular directions, including the case of Debreu-Farrell-type efficiency.