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An Adaptive Filtering Procedure for Estimating Regression Quantiles


  • Wilpen L. Gorr

    (School of Public Administration, The Ohio State University, Columbus, Ohio 43210)

  • Cheng Hsu

    (School of Management, Rensselaer Polytechnic Institute, Troy, New York 12181)


Applications of reliability theory and some forms of chance-constrained programming need real-time, nonstationary estimates of regression quantiles to trigger preventive actions, thereby avoiding undesirable system states. We have designed the Quantile Estimation Procedure (QEP) for this purpose. QEP is a new adaptive filter that nonparametrica11y estimates time-varying parameters of multivariate regression quantiles. Results of Monte Carlo tests show that QEP provides accurate estimates for a range of stochastic processes. Falling within this range is the case study of this paper on monitoring compliance with short-term air quality standards.

Suggested Citation

  • Wilpen L. Gorr & Cheng Hsu, 1985. "An Adaptive Filtering Procedure for Estimating Regression Quantiles," Management Science, INFORMS, vol. 31(8), pages 1019-1029, August.
  • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:8:p:1019-1029
    DOI: 10.1287/mnsc.31.8.1019

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    Cited by:

    1. James W. Taylor & Derek W. Bunn, 1999. "A Quantile Regression Approach to Generating Prediction Intervals," Management Science, INFORMS, vol. 45(2), pages 225-237, February.
    2. Taylor, James W., 2007. "Forecasting daily supermarket sales using exponentially weighted quantile regression," European Journal of Operational Research, Elsevier, vol. 178(1), pages 154-167, April.
    3. Taylor, James W. & Bunn, Derek W., 1999. "Investigating improvements in the accuracy of prediction intervals for combinations of forecasts: A simulation study," International Journal of Forecasting, Elsevier, vol. 15(3), pages 325-339, July.

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