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

Mette Asmild; Jens Leth Hougaard; Ole Bent Olesen

doi:10.1016/j.ejor.2013.03.038

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

Mette Asmild, Jens Leth Hougaard, Ole Bent Olesen

Section for Production, Markets and Policy

1 Citation (Scopus)

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.

Original language	English
Journal	European Journal of Operational Research
Volume	230
Issue number	1
Pages (from-to)	88-96
Number of pages	9
ISSN	0377-2217
DOIs	https://doi.org/10.1016/j.ejor.2013.03.038
Publication status	Published - 1 Oct 2013

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10.1016/j.ejor.2013.03.038

Cite this

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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.",

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AU - Hougaard, Jens Leth

AU - Olesen, Ole Bent

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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

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