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- W2262106779 abstract "The purpose of the paper is to introduce Bayesian analysis in the context of DEA. Two methods are proposed which are consistent with the data-generating-process implied by DEA. MCMC algorithms are proposed and implemented for both methods in the context of arti cial and real data. The two methods produce virtually the same results but the second method is by far more straightforward to implement in standard software. The second method does not even require to solve the linear programming problems of DEA." @default.
- W2262106779 created "2016-06-24" @default.
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- W2262106779 date "2013-01-01" @default.
- W2262106779 modified "2023-09-23" @default.
- W2262106779 title "Likelihood-Based Inference in Data Envelopment Analysis" @default.
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- W2262106779 doi "https://doi.org/10.2139/ssrn.2214616" @default.
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