A predictive DEA model for outlier detection

【作者(必填)】Yang · Wan · Zheng

【文题(必填)】 A predictive DEA model for outlier detection  

【年份(必填)】2014

【全文链接或数据库名称(选填)】


Article: A predictive DEA model for outlier detection  

Yang · Wan · Zheng


ABSTRACT: Outlier detection is one of the key issues in any data-driven analytics. In this paper, we propose Bi-super DEA, a super DEA-based method that constructs both efficient and inefficient frontiers for outlier detection. In evaluating its predictive performance, we develop a novel predictive DEA procedure, PDEA, which extends the conventional DEA approaches that have been primarily used for in-sample efficiency estimation, to predict outputs for the out-of-sample. This enables us to compare the predictive performance of our approach against several popular outlier detection methods including the parametric robust regression in statistics and non-parametric k-means in data mining. We conduct comprehensive simulation experiments to examine the relative performance of these outlier detection methods under the influence of five factors: sample size, linearity of production function, normality of noise distribution, homogeneity of data, and levels of random noise contaminating the data generating process (DGP). We find that, somewhat surprisingly, Bi-super CCR consistently outperforms Bi-super BCC in detecting outliers. Under the linearity, normality and homogeneity conditions, the parametric robust regression method works best. However, when the DGP violates these conditions, Bi-super DEA emerges as the better choice due to its distribution-free property. Our results shed light on the conditions that each method excels or fails and provide users with practical guidelines on how to choose appropriate methods to detect outliers.

No preview · Article · May 2014