William W. Cooper

Bio: 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.

A goal-focusing approach to analysis of intergenerational transfers of income

Abstract: In place of classical utility conceptions, policy discussions for intergenerational transfers of income involve the balancing of several desirable social goals: maintaining or improving the standard of living of (i) the working popuuion, (ii) the retired population, and (iii) maintaining a stable intergenerational transfer system. The new method of goal focusing achieves both the trade-off analyses of utility function methods at Pareto-efficient points and due accounting for the effects of multiple objectives. A goal-focusing approach is herein developed for the detailed quantitative analysis of intergenerational income transfers of a national social security system.

Semi-infinite programming, differentiability and geometric programming: part ii

Abstract: The authors deal with a certain specialization of their theory of duality on the case where the objective function is simple continuously differentiable and convex on the set $K$ of the admissible solutions and the constraint functions defining $K$ are continuously differentiable and concave. Further, a way is shown how to generalize the account to the case where the constraint functions of the problem are simple piecewise differentiable and concave. The obtained conditions can be considered as a generalization of Kuhn-Tucher's theorem.

Extensions to DEA Models

Abstract: The basic DEA models discussed in the previous chapter are associated with the way the returns-to-scale, the geometry of the envelopment surface, and the efficient projections are identified This array of choices provides considerable flexibility, which can be further increased by incorporating refinements or extensions to the basic theory These valuable additions to the methodology of DEA allow one to fine tune an analysis to reflect managerial or organization factors, sharpen efficiency estimates, and/or overcome inconsistencies

Some implications of the newer analytic approaches to management

Abstract: The liaison between science and management, which has made terms such as linear programming and statistical decision theory part of the business vocabulary, is now being extended in this country and abroad to open new horizons for business and the scientist. Here is a quick roundup of developments to date and a look into the future.

Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis

Abstract: In management contexts, mathematical programming is usually used to evaluate a collection of possible alternative courses of action en route to selecting one which is best. In this capacity, mathematical programming serves as a planning aid to management. Data Envelopment Analysis reverses this role and employs mathematical programming to obtain ex post facto evaluations of the relative efficiency of management accomplishments, however they may have been planned or executed. Mathematical programming is thereby extended for use as a tool for control and evaluation of past accomplishments as well as a tool to aid in planning future activities. The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs. A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data. Technical inefficiencies are identified with failures to achieve best possible output levels and/or usage of excessive amounts of inputs. Methods for identifying and correcting the magnitudes of these inefficiencies, as supplied in prior work, are illustrated. In the present paper, a new separate variable is introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decreasing returns to scale in multiple input and multiple output situations. The results are discussed and related not only to classical single output economics but also to more modern versions of economics which are identified with "contestable market theories."

Fuzzy Set Theory - and Its Applications

Abstract: Fuzzy Set Theory - And Its Applications, Third Edition is a textbook for courses in fuzzy set theory. It can also be used as an introduction to the subject. The character of a textbook is balanced with the dynamic nature of the research in the field by including many useful references to develop a deeper understanding among interested readers. The book updates the research agenda (which has witnessed profound and startling advances since its inception some 30 years ago) with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research. All chapters have been updated. Exercises are included.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software

Abstract: List of Tables. List of Figures. Preface. 1. General Discussion. 2. The Basic CCR Model. 3. The CCR Model and Production Correspondence. 4. Alternative DEA Models. 5. Returns to Scale. 6. Models with Restricted Multipliers. 7. Discretionary, Non-Discretionary and Categorical Variables. 8. Allocation Models. 9. Data Variations. Appendices. Index.