Nonparametric additive regression for repeatedly measured data

• 成果类型: Article
• 署名作者: Carroll, Raymond J.; Maity, Arnab; Mammen, Enno; Yu, Kyusang
• 署名单位: Texas A&M University System; Texas A&M University College Station; Harvard University; Harvard T.H. Chan School of Public Health; University of Mannheim
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asp015
• 发表日期: 2009
• 页码: 383398
• 关键词: clustered data; models

摘要

We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology.