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Titlebook: Estimating Output-Specific Efficiencies; Dieter Gstach Book 2002 Springer Science+Business Media Dordrecht 2002 Markov Chain.Monte Carlo S

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https://doi.org/10.1057/9780230376687e idea behind the mechanics of the present application. The interested reader will find many textbooks covering the theory in detail, for example Gilks et al., 1996b or Gamerman, 1997. Note also that the present application is formulated in a Bayesian framework, while Markov chain Monte Carlo applic
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https://doi.org/10.1057/9781137035110of a convex production technology satisfying strong disposability. This technology will be primarily described by Farrell output-efficient boundaries ..(.) depending on input vector . ∊ ℝ.. An output-ratio vector . = {.......} defined as .. = ../Σ... for . = 1... . together with . then uniquely dete
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Hester van Herk,Carlos J. Torelliresentation of ..(..,..) is missing. This is exactly the case with a DEA estimated frontier. So parameter estimation based on analytical evaluation of this likelihood in applied work (with unknown frontier) is impossible.
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IdentificationTo discuss the issue of identifiability we need the marginal likelihood of observing the sample outputs . = {..... ..} for given sample inputs . = {..... ..} and given parameters of the involved density functions.
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Chung-Shen Kuo,Wenliang Lu,Yao-Lin Kui a straightforward extension of DEA ideas. Then a simple procedure for bias correction of DEA frontier point estimates will be sketched, because the standard estimates are known to be biased under any of the potentially underlying statistical structures and because such bias correction will also underly the reported results in Chapter 8.
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