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A method for choosing the smoothing parameter in a semi-parametric model for detecting change-points in blood flow

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  • Sung Wan Han
  • Rickson C. Mesquita
  • Theresa M. Busch
  • Mary E. Putt

Abstract

In a smoothing spline model with unknown change-points, the choice of the smoothing parameter strongly influences the estimation of the change-point locations and the function at the change-points. In a tumor biology example, where change-points in blood flow in response to treatment were of interest, choosing the smoothing parameter based on minimizing generalized cross-validation (GCV) gave unsatisfactory estimates of the change-points. We propose a new method, aGCV, that re-weights the residual sum of squares and generalized degrees of freedom terms from GCV. The weight is chosen to maximize the decrease in the generalized degrees of freedom as a function of the weight value, while simultaneously minimizing aGCV as a function of the smoothing parameter and the change-points. Compared with GCV, simulation studies suggest that the aGCV method yields improved estimates of the change-point and the value of the function at the change-point.

Suggested Citation

  • Sung Wan Han & Rickson C. Mesquita & Theresa M. Busch & Mary E. Putt, 2014. "A method for choosing the smoothing parameter in a semi-parametric model for detecting change-points in blood flow," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 26-45, January.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:26-45
    DOI: 10.1080/02664763.2013.830085
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    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, November.
    2. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    3. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, November.
    4. Wahba, Grace & Wang, Yuedong, 1995. "Behavior near zero of the distribution of GCV smoothing parameter estimates," Statistics & Probability Letters, Elsevier, vol. 25(2), pages 105-111, November.
    5. Rice, John, 1986. "Convergence rates for partially splined models," Statistics & Probability Letters, Elsevier, vol. 4(4), pages 203-208, June.
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