Titlebook: Data Science and Productivity Analytics; Vincent Charles,Juan Aparicio,Joe Zhu Book 2020 Springer Nature Switzerland AG 2020 Productivity

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Data Envelopment Analysis and Big Data: Revisit with a Faster Method,ork provides the fastest available technique in the DEA literature to deal with big data. It is well known that as the number of decision-making units (DMUs) or the number of inputs–outputs increases, the size of DEA linear programming problems increases; and thus, the elapsed time to evaluate the p

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,Data Envelopment Analysis (DEA): Algorithms, Computations, and?Geometry,have a broad impact within and beyond the field. Algorithmic, computational, and geometric results in DEA allow us to solve larger problems faster; they also contribute to various other fields including computational geometry, statistics, and machine learning. This chapter reviews these topics from

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Data Envelopment Analysis and Non-parametric Analysis,on the non-parametric derivation of the Production Possibility Set (PPS), on the multiplicity of DEA models and on how to handle different types of situations, namely, undesirable outputs, ratio variables, multi-period data, negative data non-discretionary variables, and integer variables.

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