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Wavelet‐based estimation of a discriminant function

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  • Woojin Chang
  • Seong‐Hee Kim
  • Brani Vidakovic

Abstract

In this paper, we consider wavelet‐based binary linear classifiers. Both consistency results and implementational issues are addressed. We show that under mild assumptions on the design density wavelet discrimination rules are L2‐consistent. The proposed method is illustrated on synthetic data sets in which the ‘truth’ is known and on an applied discrimination problem from the industrial field. Copyright © 2003 John Wiley & Sons, Ltd.

Suggested Citation

  • Woojin Chang & Seong‐Hee Kim & Brani Vidakovic, 2003. "Wavelet‐based estimation of a discriminant function," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 19(3), pages 185-198, July.
    • Handle: RePEc:wly:apsmbi:v:19:y:2003:i:3:p:185-198
    DOI: 10.1002/asmb.498
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    Cited by:

    1. L. A. McSweeney, 2006. "Monitoring paper production using a spectral control chart designed to detect in the presence of multiple cycles," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(5), pages 467-480.
    2. Michel H. Montoril & Woojin Chang & Brani Vidakovic, 2019. "Wavelet-Based Estimation of Generalized Discriminant Functions," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 318-349, December.

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