Detecting Outliers in Deterministic Nonparametric Frontier Models With Multiple Outputs
提出一种统计方法，用于识别多投入多产出生产数据中的异常值，帮助研究者发现可能含有测量误差的观测点，并在数据核查成本高时按异常程度排序优先检查。
This article provides a statistical methodology for identifying outliers in production data with multiple inputs and outputs used in deterministic nonparametric frontier models. The methodology is useful in identifying observations that may contain some form of measurement error and thus merit closer scrutiny. When data checking is costly, the methodology may be used to rank observations in terms of their dissimilarity to other observations in the data, suggesting a priority for further inspection of the data.