Examining Data Homogeneity and Convexity for DEA Studies

• Main Author: 郭晉嘉
• Other Authors: 陳文智
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/08811208343395342735

碩士 === 國立交通大學 === 工業工程與管理系所 === 95 === Data quality is an important issue for all empirical studies including data envelopment analysis (DEA). Homogeneity and convexity are two fundamental assumptions for widely used DEA models such as BCC models (Banker et al., 1984) and CCR models (Charnes et al., 1978). This study presents two methods to examine data homogeneity and convexity for DEA studies, respectively. Relying on well-known “leave-one-out” idea, a quantitative metric on the volume and surface area of a convex hull is adopted and implemented to detect outliers, which is non-homogeneous to the rest of others, from a given data set. The deviations between convexity based and non-convexity based models can be used to infer the shape of the production technology and examine the data convexity.