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Regularized semiparametric model identification with application to nuclear magnetic resonance signal quantification with unknown macromolecular base‐line

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  • Diana M. Sima
  • Sabine Van Huffel

Abstract

Summary. We formulate and solve a semiparametric fitting problem with regularization constraints. The model that we focus on is composed of a parametric non‐linear part and a nonparametric part that can be reconstructed via splines. Regularization is employed to impose a certain degree of smoothness on the nonparametric part. Semiparametric regression is presented as a generalization of non‐linear regression, and all important differences that arise from the statistical and computational points of view are highlighted. We motivate the problem formulation with a biomedical signal processing application.

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  • Diana M. Sima & Sabine Van Huffel, 2006. "Regularized semiparametric model identification with application to nuclear magnetic resonance signal quantification with unknown macromolecular base‐line," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 383-409, June.
    • Handle: RePEc:bla:jorssb:v:68:y:2006:i:3:p:383-409
    DOI: 10.1111/j.1467-9868.2006.00550.x
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