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Minimax regret priors for efficiency estimation

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Minimax regret priors for efficiency estimation. / Tsionas, Mike.
In: European Journal of Operational Research, Vol. 309, No. 3, 16.09.2023, p. 1279-1285.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tsionas, M 2023, 'Minimax regret priors for efficiency estimation', European Journal of Operational Research, vol. 309, no. 3, pp. 1279-1285. https://doi.org/10.1016/j.ejor.2023.02.004

APA

Tsionas, M. (2023). Minimax regret priors for efficiency estimation. European Journal of Operational Research, 309(3), 1279-1285. https://doi.org/10.1016/j.ejor.2023.02.004

Vancouver

Tsionas M. Minimax regret priors for efficiency estimation. European Journal of Operational Research. 2023 Sept 16;309(3):1279-1285. Epub 2023 Apr 25. doi: 10.1016/j.ejor.2023.02.004

Author

Tsionas, Mike. / Minimax regret priors for efficiency estimation. In: European Journal of Operational Research. 2023 ; Vol. 309, No. 3. pp. 1279-1285.

Bibtex

@article{4d1dc5fef73a447a9822b5c99c37f060,
title = "Minimax regret priors for efficiency estimation",
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.",
keywords = "Productivity and competitiveness, Stochastic frontier models, Minimax regret prior, Data envelopment analysis",
author = "Mike Tsionas",
year = "2023",
month = sep,
day = "16",
doi = "10.1016/j.ejor.2023.02.004",
language = "English",
volume = "309",
pages = "1279--1285",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "3",

}

RIS

TY - JOUR

T1 - Minimax regret priors for efficiency estimation

AU - Tsionas, Mike

PY - 2023/9/16

Y1 - 2023/9/16

N2 - 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.

AB - 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.

KW - Productivity and competitiveness

KW - Stochastic frontier models

KW - Minimax regret prior

KW - Data envelopment analysis

U2 - 10.1016/j.ejor.2023.02.004

DO - 10.1016/j.ejor.2023.02.004

M3 - Journal article

VL - 309

SP - 1279

EP - 1285

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 3

ER -