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

Sima Diana M. | Zendy; Van Huffel Sabine | Zendy

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

Author(s) -

Sima Diana M.,

Van Huffel Sabine

Publication year - 2006

Publication title -

journal of the royal statistical society: series b (statistical methodology)

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 6.523

H-Index - 137

eISSN - 1467-9868

pISSN - 1369-7412

DOI - 10.1111/j.1467-9868.2006.00550.x

Subject(s) - nonparametric statistics , regularization (linguistics) , semiparametric regression , semiparametric model , parametric statistics , nonparametric regression , parametric model , computer science , mathematics , mathematical optimization , regression , algorithm , econometrics , artificial intelligence , statistics

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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