Smoothing spline estimation in varying-coefficient models

• 成果类型: Article
• 署名作者: Eubank, RL; Huang, CF; Maldonado, YM; Wang, N; Wang, S; Buchanan, RJ
• 署名单位: Texas A&M University System; Texas A&M University College Station; North Dakota State University Fargo; Texas A&M University System; Texas A&M University College Station; University of North Carolina; University of North Carolina Charlotte
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2004.B5595.x
• 发表日期: 2004
• 页码: 653-667
• 关键词: bayesian confidence-intervals; Cross-validation; regression

摘要

Smoothing spline estimators are considered for inference in varying-coefficient models with one effect modifying covariate. Bayesian 'confidence intervals' are developed for the coefficient curves and efficient computational methods are derived for computing the curve estimators, fitted values, posterior variances and data-adaptive methods for selecting the levels of Smoothing. The efficacy and utility of the methodology proposed are demonstrated through a small simulation study and the analysis of a real data set.