Titlebook: Data Envelopment Analysis; A Handbook of Models Joe Zhu Book 2015 Springer Science+Business Media New York 2015 DEA.DEA Models.Data Envelop

Indent

Jennifer L. Joscelyn MS,Lloyd Kasperchnology (or technology gap) ratio and the global frontier difference index. The latter is subsequently used to define a global Malmquist index, as well as in an alternative decomposition of the traditional Malmquist index which also considers socalled favourability and favourability change componen

multiply

FELON

DEA Cross Efficiency Under Variable Returns to Scale,series of CRS models under translated Cartesian coordinate systems. We illustrate this approach using a simple example and show how the VRS negative cross-efficiency problem is addressed under the new framework.

Indigence

maladorit

DEA Models with Production Trade-offs and Weight Restrictions,assessed using other methods. In this chapter we discuss the assessment of production trade-offs, provide the corresponding theoretical developments and suggest computational methods suitable for the solution of the resulting DEA models.

Individual

Facet Analysis in Data Envelopment Analysis,et al. Eur J Oper Res 2:429–444, 1978; Banker et al. Manage Sci 30(9):1078–1092, 1984) of the efficient frontier we derive a dual representation of the technologies. This dual representation is derived from polar cones. Relying on the characterization of efficient faces and facets in Steuer (Multipl

粘土

Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework,xamines quantile estimation using StoNED and an extension of the StoNED method to the general case of multiple inputs and multiple outputs. This chapter also provides a detailed discussion of how to model heteroscedasticity in the inefficiency and noise terms.

不在灌木丛中

DEA Based Benchmarking Models,oaches where one set of DMUs is compared (or benchmarked) against another. One approach is called “context-dependent” DEA where a set of DMUs is evaluated against a particular evaluation context. Each evaluation context represents an efficient frontier composed by DMUs in a specific performance leve

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Data Envelopment Analysis with Non-Homogeneous DMUs,output (e.g. substituting zero, or by taking the average of known values), and use it to fill in the gaps. In the present setting, however, the issue isn’t that the data for the output is missing for certain DMUs, but rather that the output isn’t produced. We argue herein that if a DMU has chosen no

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