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An algorithm for generalized monotonic smoothing

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  • Zheng Wang

Abstract

In this paper, an algorithm for Generalized Monotonic Smoothing (GMS) is developed as an extension to exponential family models of the monotonic smoothing techniques proposed by Ramsay (1988, 1998a,b). A two-step algorithm is used to estimate the coefficients of bases and the linear term. We show that the algorithm can be embedded into the iterative re-weighted least square algorithm that is typically used to estimate the coefficients in Generalized Linear Models. Thus, the GMS estimator can be computed using existing routines in S-plus and other statistical software. We apply the GMS model to the Down's syndrome data set and compare the results with those from Generalized Additive Model estimation. The choice of smoothing parameter and testing of monotonicity are also discussed.

Suggested Citation

  • Zheng Wang, 2000. "An algorithm for generalized monotonic smoothing," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(4), pages 495-507.
  • Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:495-507
    DOI: 10.1080/02664760050003678
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    Cited by:

    1. Wei Wang & Dylan S. Small, 2015. "Monotone B-Spline Smoothing for a Generalized Linear Model Response," The American Statistician, Taylor & Francis Journals, vol. 69(1), pages 28-33, February.

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