<?xml version="1.0" encoding="UTF-8"?>
<records>
<record>
<language>eng</language>
<publisher>Science and Education Publishing</publisher>
<journalTitle>American Journal of Applied Mathematics and Statistics</journalTitle>
<eissn>2328-7292</eissn>
<publicationDate>2013-07-04</publicationDate>
<volume>1</volume>
<issue>1</issue>
<startPage>57</startPage>
<endPage>63</endPage>
<doi>10.12691/ajams-1-4-2</doi>
<publisherRecordId>AJAMS2013142</publisherRecordId>
<documentType>article</documentType>
<title language="eng">Stochastic DEA with a Perfect Object and Its Application to Analysis of Environmental Efficiency</title>
<authors>
<author>
<name>Alexander Y. Vaninsky</name>
<email>avaninsky@hostos.cuny.edu</email>
<affiliationId>1</affiliationId>
</author>
</authors>
<affiliationsList>
<affiliationName affiliationId="1">Mathematics Department, City University of New York/Hostos Community College, New York, USA</affiliationName>

</affiliationsList>
<abstract language="eng">The paper introduces stochastic DEA with a Perfect Object (SDEA PO). The Perfect Object (PO) is a virtual Decision Making Unit (DMU) that has the smallest inputs and greatest outputs. Including the PO in a collection of actual objects yields an explicit formula of the efficiency index. Given the distributions of DEA inputs and outputs, this formula allows us to derive the probability distribution of the efficiency score, to find its mathematical expectation, and to deliver common (group¨Crelated) and partial (object-related) efficiency components. We apply this approach to a prospective analysis of environmental efficiency of the major national and regional economies.</abstract>
<fullTextUrl format="pdf">http://pubs.sciepub.com/ajams/1/4/2/ajams-1-4-2.pdf</fullTextUrl>
<keywords language="eng"><keyword>Data Envelopment Analysis</keyword>
<keyword>DEA analytical solutions</keyword>
<keyword>stochastic DEA with a perfect object</keyword>
<keyword>efficiency decomposition</keyword>
<keyword>environmental efficiency</keyword>
</keywords>
</record>
</records>
