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

尊嚴(yán)

https://doi.org/10.1007/978-3-642-18552-6 countries participating in PISA 2015. Given that the context in which schools are operating might be heterogeneous, we need to account for those divergences in the environmental conditions when estimating the efficiency measures of school performance. In this way, each school can be benchmarked wit

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https://doi.org/10.1007/978-3-642-18552-6e competitiveness in emerging markets of Latin America. Therefore, it is relevant to carry out an analysis of their performance. A good approach to measure performance is DEA, a mathematical tool that handles a benchmark analysis by an evaluation of multiple factors that describes the nature of an e

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https://doi.org/10.1007/978-3-642-18552-68, the investigated bank implemented a personal business-preferred policy. We established two Data Envelopment Analysis (.) models to test the impacts of . on the implementation of the policy. The results show that internal-controlled branches tend to be more sensitive to the bank’s policy. When it

AMITY

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https://doi.org/10.1007/978-3-642-18552-6 rapid growth that has defined the country’s recent past. However, little work has been able to meaningfully reflect the collaborative way in which provinces are assigned targets on a subnational regional basis. Suggesting a meta-frontier allocation approach by using data envelopment analysis (DEA),

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https://doi.org/10.1007/978-3-642-18552-6the same “culture”. However, in the real world, managers always want to compare their products/operations with similar entities (competitors), although, with some differences but in the same industry. It happens that there does not exist a model that can appropriately consider some aspects that are

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An Introduction to Data Science and Its Applications,computer science intersect. Thus, from the perspective of the data itself, machine learning, operation research, methods and algorithms, and data mining techniques are aligned to address new challenges characterised by the complexity, volume and heterogeneous nature of data.

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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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