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William W. Cooper
Researcher at University of Texas at Austin
Publications - 254
Citations - 82692
William W. Cooper is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Data envelopment analysis & Linear programming. The author has an hindex of 79, co-authored 254 publications receiving 76641 citations. Previous affiliations of William W. Cooper include Harvard University & Carnegie Mellon University.
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Equivalence and implementation of alternative methods for determining returns to scale in data envelopment analysis
TL;DR: In this paper, the authors discuss alternative methods for determining returns to scale in DEA, based on Banker's concept of Most Productive Scale Size (MPSS), which is equivalent to the two-stage methods of Fare, Grosskopf and Lovell.
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Choosing weights from alternative optimal solutions of dual multiplier models in dea
TL;DR: A two-step procedure to be used for the selection of the weights that are obtained from the multiplier model in a DEA efficiency analysis to explore the set of alternate optima in order to help make a choice of optimal weights.
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Measures of inefficiency in data envelopment analysis and stochastic frontier estimation
William W. Cooper,Kaoru Tone +1 more
TL;DR: In this paper, the authors discuss recent work in developing scalar measures of inefficiency which comprehend all inefficiencies, including non-zero slacks, and are readily interpretable and easily used in a wide variety of contexts.
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Evaluation of Educational Program Proposals by Means of DEA
Authella M. Bessent,E. Wailand Bessent,Abraham Charnes,William W. Cooper,Nellie Carr Thorogood +4 more
TL;DR: A new application of DEA (Data Envelopment Analysis) is examined for evaluating the efficiency of occupational-technical programs in a comprehensive community college.
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Duality in Semi-Infinite Programs and some Works of Haar and Caratheodory
TL;DR: In this article, an extended dual theorem comparable in precision and exhaustiveness to the finite space theorem is developed for arbitrary convex programs with convex constraints which subsumes in principle all characterizations of optimality or duality in convex programming.