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Title: Regularized semiparametric model identification with application to nuclear magnetic resonance signal quantification with unknown macromolecular base-line
Authors: Sima, Diana ×
Van Huffel, Sabine #
Issue Date: 2006
Publisher: Royal Statistical Society
Series Title: Journal of the Royal Statistical Society B, Statistical Methodology vol:68 issue:3 pages:383-409
Abstract: 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.
ISSN: 1369-7412
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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