An Introduction to Data Envelopment Analysis: A Tool for Performance Measurement

By R. Ramanathan. 2003

515 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/82b1055ddff17b306f5a046e2c8b2fb63a82c2bd

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Citations in the corpus

Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process (2006, background): “The paper ends with a summary and conclusions of the paper.”
Technical efficiency in farming: a meta-regression analysis (2007, background): “Another characteristic of DEA methods is the potential sensitivity of efficiency scores to the number of observations as well as to the dimensionality of the frontier (Ramanathan 2003).”
A survey of data envelopment analysis in energy and environmental studies (2008, methodology): “For the RTS property of reference technology, it is found that about a half of the studies assumed that the reference technology exhibits CRS, although VRS might be a more appropriate assumption (Ramanathan, 2003).”
The debate on flexibility of environmental regulations, innovation capabilities and financial performance : a novel use of DEA (2018, methodology): “The DEA literature has suggested that one way of classifying a factor as an output would be to check whether units recording higher performance in terms of that factor would be considered more efficient or not (Golany and Roll, 1989; Dyson et al., 2001; Ramanathan, 2003).”
A mathematical programming approach to constructing composite indicators (2007, methodology): “…can be used to derive objective weights for use.1 DEA, developed byCharnes et al. (1978), is awell established non-parametric technique to evaluate the relative efficiencies of a set of comparable entities by some specific mathematical programming models (Ramanathan, 2003; Cooper et al., 2006).”