Data Envelopment Analysis (DEA) is often overlooked in empirical
work such as diagnostic tests to determine whether the data conform
with technology which, in turn, is important in identifying
technical change, or finding which types of DEA models allow data
transformations, including dealing with ordinal data.Advances in
Data Envelopment Analysis focuses on both theoretical developments
and their applications into the measurement of productive
efficiency and productivity growth, such as its application to the
modelling of time substitution, i.e. the problem of how to allocate
resources over time, and estimating the 'value' of a Decision
Making Unit (DMU).
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