IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v52y2006i1p128-135.html
   My bibliography  Save this article

Testing the Statistical Significance of Linear Programming Estimators

Author

Listed:
  • Dan Horsky

    (William E. Simon Graduate School of Business Administration, University of Rochester, P.O. Box 270100, Rochester, New York 14627)

  • Paul Nelson

    (William E. Simon Graduate School of Business Administration, University of Rochester, P.O. Box 270100, Rochester, New York 14627)

Abstract

Linear programming-based estimation procedures are used in a variety of arenas. Two notable areas are multiattribute utility models (LINMAP) and production frontiers (data envelopment analysis (DEA)). Both LINMAP and DEA have theoretical and managerial advantages. For example, LINMAP treats ordinal-scaled preference data as such in uncovering individual-level attribute weights, while regression treats these preferences as interval scaled. DEA produces easy-to-understand efficiency measures, which allow for improved productivity benchmarking. However, acceptance of these techniques is hindered by the lack of statistical significance tests for their parameter estimates. In this paper, we propose and evaluate such parameter significance tests. Two types of tests are forwarded. The first examines whether a model's fit is significantly reduced when an explanatory variable is deleted. The second is based on generating a standard deviation or distribution for the parameter estimate using nonparametric jackknife or bootstrap techniques. We demonstrate through simulations that both types of tests reliably identify both significant and insignificant parameters. The availability of these tests, especially the relatively simple and easy-to-use tests of the first type, should enhance the utilization of linear programming-based estimation.

Suggested Citation

  • Dan Horsky & Paul Nelson, 2006. "Testing the Statistical Significance of Linear Programming Estimators," Management Science, INFORMS, vol. 52(1), pages 128-135, January.
    • Handle: RePEc:inm:ormnsc:v:52:y:2006:i:1:p:128-135
    DOI: 10.1287/mnsc.1050.0444
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.1050.0444
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.1050.0444?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
    2. Wagner A. Kamakura & Rajendra K. Srivastava, 1986. "An Ideal-Point Probabilistic Choice Model for Heterogeneous Preferences," Marketing Science, INFORMS, vol. 5(3), pages 199-218.
    3. Dan Horsky & M. R. Rao, 1984. "Estimation of Attribute Weights from Preference Comparisons," Management Science, INFORMS, vol. 30(7), pages 801-822, July.
    4. Dan Horsky & Paul Nelson, 1996. "Evaluation of Salesforce Size and Productivity Through Efficient Frontier Benchmarking," Marketing Science, INFORMS, vol. 15(4), pages 301-320.
    5. John R. Hauser & Steven M. Shugan, 1980. "Intensity Measures of Consumer Preference," Operations Research, INFORMS, vol. 28(2), pages 278-320, April.
    6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    7. David S. Evans & James J. Heckman, 1988. "Rejoinder---Natural Monopoly and the Bell System: Response to Charnes, Cooper and Sueyoshi," Management Science, INFORMS, vol. 34(1), pages 27-38, January.
    8. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
    9. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    10. A. Charnes & W. W. Cooper & T. Sueyoshi, 1988. "A Goal Programming/Constrained Regression Review of the Bell System Breakup," Management Science, INFORMS, vol. 34(1), pages 1-26, January.
    11. David Klahr, 1969. "A monte carlo investigation of the statistical significance of Kruskal's nonmetric scaling procedure," Psychometrika, Springer;The Psychometric Society, vol. 34(3), pages 319-330, September.
    12. V. Srinivasan & Allan Shocker, 1973. "Linear programming techniques for multidimensional analysis of preferences," Psychometrika, Springer;The Psychometric Society, vol. 38(3), pages 337-369, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Franklin Dexter & Liam O’Neill & Lei Xin & Johannes Ledolter, 2008. "Sensitivity of super-efficient data envelopment analysis results to individual decision-making units: an example of surgical workload by specialty," Health Care Management Science, Springer, vol. 11(4), pages 307-318, December.
    2. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
    3. Jessica Rubiano-Moreno & Samuel Nucamendi-Guillén & Alvaro Cordero-Franco & Alejandro Rodríguez-Magaña, 2022. "An improved LINMAP for multicriteria decision: designing customized incentive portfolios in an organization," Operational Research, Springer, vol. 22(4), pages 3489-3520, September.
    4. N. Avkiran, 2010. "Sensitivity analysis of network DEA illustrated in branch banking," CEPA Working Papers Series WP122010, School of Economics, University of Queensland, Australia.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    2. Chang, Hsi-Hui, 1998. "Determinants of Hospital Efficiency: the Case of Central Government-owned Hospitals in Taiwan," Omega, Elsevier, vol. 26(2), pages 307-317, April.
    3. Kuosmanen, Timo & Post, Thierry & Scholtes, Stefan, 2007. "Non-parametric tests of productive efficiency with errors-in-variables," Journal of Econometrics, Elsevier, vol. 136(1), pages 131-162, January.
    4. Carlos Pestana Barros & Maria Teresa Medeiros Garcia, 2006. "Performance Evaluation of Pension Funds Management Companies with Data Envelopment Analysis," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 9(2), pages 165-188, September.
    5. E Zere & Diane Mcintyre & T Addison, 2001. "Technical Efficiency And Productivity Of Public Sector Hospitals In Three South African Provinces," South African Journal of Economics, Economic Society of South Africa, vol. 69(2), pages 336-358, June.
    6. A. M. Theodoridis & A. Psychoudakis, 2008. "Efficiency Measurement in Greek Dairy Farms: Stochastic Frontier vs. Data Envelopment Analysis," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 1(2), pages 53-67, December.
    7. Fadzlan Sufian, 2016. "Determinants of Efficiency in the Malaysian Banking Sector: Evidence from Semi-parametric Data Envelopment Analysis Method," Studies in Microeconomics, , vol. 4(2), pages 151-172, December.
    8. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    9. Gounopoulos, Dimitrios & Kallias, Konstantinos & Newton, David & Tzeremes, Nickolaos, 2016. "Political connections and IPO underpricing: An efficiency problem," MPRA Paper 69427, University Library of Munich, Germany.
    10. Antonio Affuso & Cataldo Ferrarese & Guido Nannariello, 2013. "Spending Review: un?analisi di efficienza delle Capitanerie di porto," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2013(3), pages 111-143.
    11. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "Data envelopment analysis 1978–2010: A citation-based literature survey," Omega, Elsevier, vol. 41(1), pages 3-15.
    12. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    13. Chenini Hajer & Jarboui Anis, 2018. "Analysis of the Impact of Governance on Bank Performance: Case of Commercial Tunisian Banks," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 9(3), pages 871-895, September.
    14. Adler, Nicole & Friedman, Lea & Sinuany-Stern, Zilla, 2002. "Review of ranking methods in the data envelopment analysis context," European Journal of Operational Research, Elsevier, vol. 140(2), pages 249-265, July.
    15. Noel Uri, 2003. "The Effect of Incentive Regulation in Telecommunications in the United States," Quality & Quantity: International Journal of Methodology, Springer, vol. 37(2), pages 169-191, May.
    16. Silvia Saravia-Matus & T. S. Amjath-Babu & Sreejith Aravindakshan & Stefan Sieber & Jimmy A. Saravia & Sergio Gomez y Paloma, 2021. "Can Enhancing Efficiency Promote the Economic Viability of Smallholder Farmers? A Case of Sierra Leone," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    17. Cherchye, L. & Post, G.T., 2001. "Methodological Advances in Dea," ERIM Report Series Research in Management ERS-2001-53-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    18. María Victoria Uribe‐Bohorquez & Jennifer Martínez‐Ferrero & Isabel‐María García‐Sánchez, 2019. "Women on boards and efficiency in a business‐orientated environment," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 26(1), pages 82-96, January.
    19. J. Cummins & Hongmin Zi, 1998. "Comparison of Frontier Efficiency Methods: An Application to the U.S. Life Insurance Industry," Journal of Productivity Analysis, Springer, vol. 10(2), pages 131-152, October.
    20. Pontus Mattsson & Jonas Månsson & Christian Andersson & Fredrik Bonander, 2018. "A bootstrapped Malmquist index applied to Swedish district courts," European Journal of Law and Economics, Springer, vol. 46(1), pages 109-139, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:52:y:2006:i:1:p:128-135. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.