Kristof De Witte

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Practitioners assess performance of entities in increasingly large and complicated datasets. If non-parametric models, such as Data Envelopment Analysis, were ever considered as simple pushbutton technologies, this is impossible when many variables are available or when data have to be compiled from several sources. This paper introduces by the(More)
This paper presents a methodology to aggregate multidimensional research output. Using a tailored version of the non-parametric Data Envelopment Analysis model, we account for the large heterogeneity in research output and the individual researcher preferences by endogenously weighting the various output dimensions. The approach offers three important(More)
protection, utilities) can be characterised by a two-stage production process. In the first stage, basic inputs (e.g., labour and capital) are used to generate service potential (e.g., opening hours, materials), which is then, in the second stage, transformed into observed outputs (e.g., school outcomes, library circulation, crimes solved). As final outputs(More)
This paper suggests an outlier detection procedure which applies a nonparametric model accounting for undesired outputs and exogenous influences in the sample. Although efficiency is estimated in a deterministic frontier approach, each potential outlier initially benefits of the doubt of not being an outlier. We survey several outlier detection procedures(More)