Smoothing parameter selection in two frameworks for penalized splines

• 成果类型: Article
• 署名作者: Krivobokova, Tatyana
• 署名单位: University of Gottingen
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12010
• 发表日期: 2013
• 页码: 725-741
• 关键词: generalized cross-validation; maximum-likelihood; regression

摘要

There are two popular smoothing parameter selection methods for spline smoothing. First, smoothing parameters can be estimated by minimizing criteria that approximate the average mean-squared error of the regression function estimator. Second, the maximum likelihood paradigm can be employed, under the assumption that the regression function is a realization of some stochastic process. The asymptotic properties of both smoothing parameter estimators for penalized splines are studied and compared. A simulation study and a real data example illustrate the theoretical findings.