IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v41y1995i2p244-262.html
   My bibliography  Save this article

Simulation Designs for the Estimation of Quadratic Response Surface Gradients in the Presence of Model Misspecification

Author

Listed:
  • Joan M. Donohue

    (Department of Management Science, University of South Carolina, Columbia, South Carolina 29208-0001)

  • Ernest C. Houck

    (Department of Management Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0235)

  • Raymond H. Myers

    (Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0439)

Abstract

This article considers the construction of simulation designs for the ordinary least squares estimation of second-order metamodels. Two premises underlie the development of these experimental strategies. First it is assumed that the postulated metamodel may be misspecified due to the true model structure being of third-order. It is therefore important that the locations of the simulation experiments be specified to provide protection against bias, as well as variance, in the estimation of metamodel parameters. The second premise is based on the observation that, in many applications of metamodels, functions of the fitted model coefficients (such as the slope gradients) are of greater interest than the response function. The integrated mean squared error of slopes design criterion that is implemented here addresses both premises. This criterion finds application in various optimum seeking methods and sensitivity analysis procedures. Combinations of four important classes of response surface designs and three pseudorandom number assignment strategies constitute the basis structure of the simulation designs studied. The performance of these simulation designs is evaluated and, subsequently, compared to a similar set of experimental plans that have as their focus the estimation of the response function.

Suggested Citation

  • Joan M. Donohue & Ernest C. Houck & Raymond H. Myers, 1995. "Simulation Designs for the Estimation of Quadratic Response Surface Gradients in the Presence of Model Misspecification," Management Science, INFORMS, vol. 41(2), pages 244-262, February.
  • Handle: RePEc:inm:ormnsc:v:41:y:1995:i:2:p:244-262
    DOI: 10.1287/mnsc.41.2.244
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.41.2.244
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.41.2.244?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
    ---><---

    Citations

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


    Cited by:

    1. Kleijnen, Jack P. C. & den Hertog, Dick & Angun, Ebru, 2004. "Response surface methodology's steepest ascent and step size revisited," European Journal of Operational Research, Elsevier, vol. 159(1), pages 121-131, November.
    2. R. C. M. Brekelmans & L. T. Driessen & H. J. M. Hamers & D. den. Hertog, 2005. "Gradient Estimation Schemes for Noisy Functions," Journal of Optimization Theory and Applications, Springer, vol. 126(3), pages 529-551, September.
    3. Arsham H., 1998. "Techniques for Monte Carlo Optimizing," Monte Carlo Methods and Applications, De Gruyter, vol. 4(3), pages 181-230, December.
    4. Angun, M.E., 2004. "Black box simulation optimization : Generalized response surface methodology," Other publications TiSEM 2548e953-54ce-44e2-8c5b-7, Tilburg University, School of Economics and Management.
    5. Batmaz, Inci & Tunali, Semra, 2003. "Small response surface designs for metamodel estimation," European Journal of Operational Research, Elsevier, vol. 145(2), pages 455-470, March.
    6. Safizadeh, M. Hossein, 2002. "Minimizing the bias and variance of the gradient estimate in RSM simulation studies," European Journal of Operational Research, Elsevier, vol. 136(1), pages 121-135, January.

    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:41:y:1995:i:2:p:244-262. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.