Classification of non-parametric regression functions in longitudinal data models

• 成果类型: Article
• 署名作者: Vogt, Michael; Linton, Oliver
• 署名单位: University of Bonn; University of Cambridge
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12155
• 发表日期: 2017
• 页码: 5-27
• 关键词: k-means; Consistency

摘要

We investigate a longitudinal data model with non-parametric regression functions that may vary across the observed individuals. In a variety of applications, it is natural to impose a group structure on the regression curves. Specifically, we may suppose that the observed individuals can be grouped into a number of classes whose members all share the same regression function. We develop a statistical procedure to estimate the unknown group structure from the data. Moreover, we derive the asymptotic properties of the procedure and investigate its finite sample performance by means of a simulation study and a real data example.