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

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

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)26-45
Number of pages20
JournalJournal of Applied Statistics
Volume41
Issue number1
DOIs
Publication statusPublished - 2014 Jan

Keywords

  • change-points
  • generalized cross-validation
  • generalized degrees of freedom
  • partial spline
  • smoothing spline

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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