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

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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.

Keywords: Non-linear regression; Regularization; Semiparametric regression; Smoothing; Splines

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1467-9868.2006.00550.x

Affiliations: Katholieke Universiteit Leuven, Belgium

Publication date: June 1, 2006

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