SELECTING DEA MODEL SPECIFICATIONS AND RANKING UNITS VIA PRINCIPAL COMPONENT ANALYSIS: A APPLICATION OF ECONOMIC PERFORMANCE OF CITIES
Öz
Data Envelopment Analysis (DEA) is non-parametric mathematical tool a linear programming-based approach for measuring the relative efficiency of decision makin gunits (DMUs). DEA is becoming widely used to evaluate the efficiency of organizations with multiple homogeneous DMUs such as universities, hospitals, and banks that produce several outputs with a variety of inputs. Different model selection methods have been suggested for DEA in the literature. Model selection in DEA is a very important problem. Efficiency score of DMU takes different values based on input and output. Variable selection is crucial to the process as the omission of some of the inputs can have a large effect on efficiency score. In this study, an example deals with the efficiency in the economic performance of 28 Chinese cities. Efficiency scores are calculated for all possible DEA model specifications. The results are analyzed using Principal Component Analysis and a new method for model selection is proposed in this paper.
Anahtar Kelimeler
Kaynakça
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Ayrıntılar
Birincil Dil
Türkçe
Konular
-
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
6 Kasım 2016
Gönderilme Tarihi
25 Kasım 2016
Kabul Tarihi
-
Yayımlandığı Sayı
Yıl 2016