E
E Rhodes
Researcher at Indiana University
Publications - 7
Citations - 27506
E Rhodes is an academic researcher from Indiana University. The author has contributed to research in topics: Data envelopment analysis & Linear programming. The author has an hindex of 5, co-authored 6 publications receiving 24906 citations. Previous affiliations of E Rhodes include University at Buffalo.
Papers
More filters
Journal ArticleDOI
Measuring the efficiency of decision making units
TL;DR: A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs and methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs.
Journal ArticleDOI
Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through
TL;DR: A model for measuring the efficiency of Decision Making Units =DMU's is presented, along with related methods of implementation and interpretation, and suggests the additional possibility of new approaches obtained from PFT-NFT combinations which may be superior to either of them alone.
Short communication: measuring efficiency of decision making units
A Charnes,W W Cooper,E Rhodes +2 more
A Data Envelopment Analysis Approach to Evaluation of the Program Follow through Experiment in U.S. Public School Education.
A Charnes,W W Cooper,E Rhodes +2 more
TL;DR: Data Envelopment Analysis (DEA) as mentioned in this paper is used to decompose the efficiency of decision making units (DMU's) into two parts: (1) a component resulting from managerial decisions and (2) a part resulting from constraints (called programs) under which management operates.
Measuring the Efficiency of Decision Making Units with Some New Production Functions and Estimation Methods.
A Charnes,W W Cooper,E Rhodes +2 more
TL;DR: In this paper, a series of linear programming models are used to clarify and extend a measure of efficiency, and the duals to these models are shown to yield estimates of production coefficients from the same empirical data and computations that yield the measures of efficiency.