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

• 成果类型: Article
• 署名作者: Sima, Diana M.; Van Huffel, Sabine
• 署名单位: KU Leuven
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2006.00550.x
• 发表日期: 2006
• 页码: 383-409
• 关键词: inference; splines

摘要

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.