Minimax regret priors for efficiency estimation

Tsionas, Mike (2023) Minimax regret priors for efficiency estimation. European Journal of Operational Research, 309 (3). pp. 1279-1285. ISSN 0377-2217

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Abstract

We propose a minimax regret empirical prior for inefficiencies in a stochastic frontier model and for its other parameters. The class of priors over which we consider minimax regret is given by DEA interval scores and, for the parameters, the class of priors induced by maximum likelihood estimates. The new techniques are shown to perform well in a Monte Carlo study as well as in real data for large U.S. data banks.

Item Type:
Journal Article
Journal or Publication Title:
European Journal of Operational Research
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2611
Subjects:
?? productivity and competitivenessstochastic frontier modelsminimax regret priordata envelopment analysismodelling and simulationmanagement science and operations researchinformation systems and management ??
ID Code:
187676
Deposited By:
Deposited On:
28 Feb 2023 14:41
Refereed?:
Yes
Published?:
Published
Last Modified:
27 Jun 2024 00:49