用凸最小二乘回归表示模糊DEA模型

2017 International Conference on Industrial Engineering, Management Science and Application (ICIMSA) Pub Date : 2017-06-01 DOI:10.1109/ICIMSA.2017.7985612

W. Chung

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引用次数: 0

摘要

凸非参数最小二乘(CNLSs)是一种估计单调递增函数和凸函数的非参数回归技术。此外，CNLS方法建立在与数据包络分析(DEA)相同的公理基础上，并考虑了噪声。本文研究用凸最小二乘回归来表示一个模糊DEA模型。通过CNLS的结果，我们可以重复使用相应的模糊DEA模型来评估未观察到的决策单元的性能。注意，DEA结果不能重复用作未观测实体的回归结果。模糊DEA的普及程度将得到提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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The Use of Convex Least Square Regression to Represent a Fuzzy DEA Model

Convex Nonparametric Least Squares (CNLSs) is a nonparametric regression technique to estimate monotonic increasing and convex functions. In addition, CNLS method builds on the same axioms as Data Envelopment Analysis (DEA) and also takes into account noise. This paper is to investigate the use of convex least square regression to represent a fuzzy DEA model. By the results of CNLS, we can repeatedly use the corresponding fuzzy DEA model to assess the performance of unobserved decision making units. Note that DEA results cannot be repeatedly used as the regression results for unobserved entities. The popularity of fuzzy DEA would be enhanced.

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2017 International Conference on Industrial Engineering, Management Science and Application (ICIMSA)

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