• DocumentCode
    3077413
  • Title

    A criterion for model-robust design of experiments

  • Author

    Roger, Morgan ; Le Brusquet, Laurent ; Fleury, Gilles

  • Author_Institution
    Dept. of Meas., Supelec, Gif-sur-Yvette
  • fYear
    2004
  • fDate
    Sept. 29 2004-Oct. 1 2004
  • Firstpage
    33
  • Lastpage
    42
  • Abstract
    The paper considers the design of experiments for linear models with misspecification, of the form t(x) = Sigmai = 1 p thetasiPhii(x) + r(x), where r(x) is an unknown deviation from the regression model. Considering a modeling of this misspecification, the goal is to obtain robust designs which minimize the integral quadratic risk. A kernel-based representation (Gaussian process) is chosen to model the misspecification and a new criterion is derived, composed of the classical L-criterion, plus a specific term. Robust designs are then given for polynomial regression, in the particular case of a Gaussian kernel for the Gaussian process. The benefits of this approach are finally demonstrated through comparison of the performance (in terms of integral quadratic error) of such designs versus L-optimal and uniform designs on a simple illustrative example
  • Keywords
    Gaussian processes; design of experiments; linear systems; regression analysis; Gaussian kernel; Gaussian process; L-criterion; integral quadratic error; integral quadratic risk; kernel-based representation; linear models; model-robust design of experiments; polynomial regression; regression model; Design for experiments; Gaussian processes; Kernel; Polynomials; Robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning for Signal Processing, 2004. Proceedings of the 2004 14th IEEE Signal Processing Society Workshop
  • Conference_Location
    Sao Luis
  • ISSN
    1551-2541
  • Print_ISBN
    0-7803-8608-4
  • Type

    conf

  • DOI
    10.1109/MLSP.2004.1422957
  • Filename
    1422957