IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v171y2006i1p53-63.html
   My bibliography  Save this article

Statistical fitting and validation of non-linear simulation metamodels: A case study

Author

Listed:
  • Reis dos Santos, M. Isabel
  • Porta Nova, Acacio M.O.

Abstract

No abstract is available for this item.

Suggested Citation

  • Reis dos Santos, M. Isabel & Porta Nova, Acacio M.O., 2006. "Statistical fitting and validation of non-linear simulation metamodels: A case study," European Journal of Operational Research, Elsevier, vol. 171(1), pages 53-63, May.
  • Handle: RePEc:eee:ejores:v:171:y:2006:i:1:p:53-63
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(04)00612-5
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Panis, Renato P. & Myers, Raymond H. & Houck, Ernest C., 1994. "Combining regression diagnostics with simulation metamodels," European Journal of Operational Research, Elsevier, vol. 73(1), pages 85-94, February.
    2. Acácio M. De O. Porta Nova & James R. Wilson, 1989. "Estimation of Multiresponse Simulation Metamodels Using Control Variates," Management Science, INFORMS, vol. 35(11), pages 1316-1333, November.
    3. Jack P. C. Kleijnen, 1992. "Regression Metamodels for Simulation with Common Random Numbers: Comparison of Validation Tests and Confidence Intervals," Management Science, INFORMS, vol. 38(8), pages 1164-1185, August.
    4. Kleijnen, J. P. C. & van den Burg, A. J. & van der Ham, R. Th., 1979. "Generalization of simulation results practicality of statistical methods," European Journal of Operational Research, Elsevier, vol. 3(1), pages 50-64, January.
    5. Kleijnen, Jack P. C. & Sargent, Robert G., 2000. "A methodology for fitting and validating metamodels in simulation," European Journal of Operational Research, Elsevier, vol. 120(1), pages 14-29, January.
    6. Masson, Egill & Wang, Yih-Jeou, 1990. "Introduction to computation and learning in artificial neural networks," European Journal of Operational Research, Elsevier, vol. 47(1), pages 1-28, July.
    7. Robert W. Blanning, 1974. "The Sources and Uses of Sensitivity Information," Interfaces, INFORMS, vol. 4(4), pages 32-38, August.
    8. White, Halbert, 1980. "Nonlinear Regression on Cross-Section Data," Econometrica, Econometric Society, vol. 48(3), pages 721-746, April.
    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. Poropudas, Jirka & Virtanen, Kai, 2011. "Simulation metamodeling with dynamic Bayesian networks," European Journal of Operational Research, Elsevier, vol. 214(3), pages 644-655, November.
    2. Shi, Wen & Liu, Zhixue & Shang, Jennifer & Cui, Yujia, 2013. "Multi-criteria robust design of a JIT-based cross-docking distribution center for an auto parts supply chain," European Journal of Operational Research, Elsevier, vol. 229(3), pages 695-706.
    3. Shi, Wen & Shang, Jennifer & Liu, Zhixue & Zuo, Xiaolu, 2014. "Optimal design of the auto parts supply chain for JIT operations: Sequential bifurcation factor screening and multi-response surface methodology," European Journal of Operational Research, Elsevier, vol. 236(2), pages 664-676.
    4. Reis dos Santos, Pedro M. & Isabel Reis dos Santos, M., 2009. "Using subsystem linear regression metamodels in stochastic simulation," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1031-1040, August.

    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. Jack P. C. Kleijnen & Susan M. Sanchez & Thomas W. Lucas & Thomas M. Cioppa, 2005. "State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments," INFORMS Journal on Computing, INFORMS, vol. 17(3), pages 263-289, August.
    2. Kleijnen, J.P.C. & Sanchez, S.M. & Lucas, T.W. & Cioppa, T.M., 2003. "A User's Guide to the Brave New World of Designing Simulation Experiments," Discussion Paper 2003-1, Tilburg University, Center for Economic Research.
    3. Jin, Ding & Hedtrich, Johannes & Henning, Christian, 2018. "Applying Meta modeling for extended CGE-modeling: Sample techniques and potential application," Conference papers 332947, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    4. Hawre Jalal & Bryan Dowd & François Sainfort & Karen M. Kuntz, 2013. "Linear Regression Metamodeling as a Tool to Summarize and Present Simulation Model Results," Medical Decision Making, , vol. 33(7), pages 880-890, October.
    5. Reis dos Santos, Pedro M. & Isabel Reis dos Santos, M., 2009. "Using subsystem linear regression metamodels in stochastic simulation," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1031-1040, August.
    6. Kleijnen, Jack P. C., 2005. "An overview of the design and analysis of simulation experiments for sensitivity analysis," European Journal of Operational Research, Elsevier, vol. 164(2), pages 287-300, July.
    7. Ekren, Orhan & Ekren, Banu Yetkin, 2008. "Size optimization of a PV/wind hybrid energy conversion system with battery storage using response surface methodology," Applied Energy, Elsevier, vol. 85(11), pages 1086-1101, November.
    8. Poropudas, Jirka & Virtanen, Kai, 2011. "Simulation metamodeling with dynamic Bayesian networks," European Journal of Operational Research, Elsevier, vol. 214(3), pages 644-655, November.
    9. Kleijnen, Jack P.C. & Deflandre, David, 2006. "Validation of regression metamodels in simulation: Bootstrap approach," European Journal of Operational Research, Elsevier, vol. 170(1), pages 120-131, April.
    10. Ekren, Orhan & Ekren, Banu Y. & Ozerdem, Baris, 2009. "Break-even analysis and size optimization of a PV/wind hybrid energy conversion system with battery storage - A case study," Applied Energy, Elsevier, vol. 86(7-8), pages 1043-1054, July.
    11. Kleijnen, J.P.C., 1997. "Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models," Discussion Paper 1997-52, Tilburg University, Center for Economic Research.
    12. Noguera, Jose H. & Watson, Edward F., 2006. "Response surface analysis of a multi-product batch processing facility using a simulation metamodel," International Journal of Production Economics, Elsevier, vol. 102(2), pages 333-343, August.
    13. Katarzyna Growiec & Jakub Growiec & Bogumil Kaminski, 2017. "Social Network Structure and The Trade-Off Between Social Utility and Economic Performance," KAE Working Papers 2017-026, Warsaw School of Economics, Collegium of Economic Analysis.
    14. Acharki, Naoufal & Bertoncello, Antoine & Garnier, Josselin, 2023. "Robust prediction interval estimation for Gaussian processes by cross-validation method," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    15. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    16. H. Christopher Frey & Sumeet R. Patil, 2002. "Identification and Review of Sensitivity Analysis Methods," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 553-578, June.
    17. Tunali, S. & Batmaz, I., 2003. "A metamodeling methodology involving both qualitative and quantitative input factors," European Journal of Operational Research, Elsevier, vol. 150(2), pages 437-450, October.
    18. Akhand, Hafiz A., 1996. "Effective federal individual income tax functions: A specification search," Economics Letters, Elsevier, vol. 51(1), pages 19-25, April.
    19. Kleijnen, J.P.C., 1995. "Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments," Other publications TiSEM 87ee6ee0-592c-4204-ac50-6, Tilburg University, School of Economics and Management.
    20. Xiu, Dacheng, 2010. "Quasi-maximum likelihood estimation of volatility with high frequency data," Journal of Econometrics, Elsevier, vol. 159(1), pages 235-250, November.

    More about this item

    Statistics

    Access and download statistics

    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:eee:ejores:v:171:y:2006:i:1:p:53-63. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.