Testing over-representation of observations in subsets of a DEA technology

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Testing over-representation of observations in subsets of a DEA technology. / Asmild, Mette; Hougaard, Jens Leth; Olesen, Ole Bent.

In: European Journal of Operational Research, Vol. 230, No. 1, 2013, p. 88-96.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Asmild, M, Hougaard, JL & Olesen, OB 2013, 'Testing over-representation of observations in subsets of a DEA technology', European Journal of Operational Research, vol. 230, no. 1, pp. 88-96. https://doi.org/10.1016/j.ejor.2013.03.038

APA

Asmild, M., Hougaard, J. L., & Olesen, O. B. (2013). Testing over-representation of observations in subsets of a DEA technology. European Journal of Operational Research, 230(1), 88-96. https://doi.org/10.1016/j.ejor.2013.03.038

Vancouver

Asmild M, Hougaard JL, Olesen OB. Testing over-representation of observations in subsets of a DEA technology. European Journal of Operational Research. 2013;230(1):88-96. https://doi.org/10.1016/j.ejor.2013.03.038

Author

Asmild, Mette ; Hougaard, Jens Leth ; Olesen, Ole Bent. / Testing over-representation of observations in subsets of a DEA technology. In: European Journal of Operational Research. 2013 ; Vol. 230, No. 1. pp. 88-96.

Bibtex

@article{b28cd00f36db4624ac68487406af66bc,
title = "Testing over-representation of observations in subsets of a DEA technology",
abstract = "This paper proposes a test for whether data are over-represented in a given production zone, i.e. a subset of a production possibility set which has been estimated using the non-parametric Data Envelopment Analysis (DEA) approach. A binomial test is used that relates the number of observations inside such a zone to a discrete probability weighted relative volume of that zone. A Monte Carlo simulation illustrates the performance of the proposed test statistic and provides good estimation of both facet probabilities and the assumed common inefficiency distribution in a three dimensional input space. Potential applications include tests for whether benchmark units dominate more (or less) observations than expected.",
author = "Mette Asmild and Hougaard, {Jens Leth} and Olesen, {Ole Bent}",
year = "2013",
doi = "10.1016/j.ejor.2013.03.038",
language = "English",
volume = "230",
pages = "88--96",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Testing over-representation of observations in subsets of a DEA technology

AU - Asmild, Mette

AU - Hougaard, Jens Leth

AU - Olesen, Ole Bent

PY - 2013

Y1 - 2013

N2 - This paper proposes a test for whether data are over-represented in a given production zone, i.e. a subset of a production possibility set which has been estimated using the non-parametric Data Envelopment Analysis (DEA) approach. A binomial test is used that relates the number of observations inside such a zone to a discrete probability weighted relative volume of that zone. A Monte Carlo simulation illustrates the performance of the proposed test statistic and provides good estimation of both facet probabilities and the assumed common inefficiency distribution in a three dimensional input space. Potential applications include tests for whether benchmark units dominate more (or less) observations than expected.

AB - This paper proposes a test for whether data are over-represented in a given production zone, i.e. a subset of a production possibility set which has been estimated using the non-parametric Data Envelopment Analysis (DEA) approach. A binomial test is used that relates the number of observations inside such a zone to a discrete probability weighted relative volume of that zone. A Monte Carlo simulation illustrates the performance of the proposed test statistic and provides good estimation of both facet probabilities and the assumed common inefficiency distribution in a three dimensional input space. Potential applications include tests for whether benchmark units dominate more (or less) observations than expected.

U2 - 10.1016/j.ejor.2013.03.038

DO - 10.1016/j.ejor.2013.03.038

M3 - Journal article

VL - 230

SP - 88

EP - 96

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 1

ER -

ID: 45558921