Warped functional regression

• 成果类型: Article
• 署名作者: Gervini, Daniel
• 署名单位: University of Wisconsin System; University of Wisconsin Milwaukee
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu054
• 发表日期: 2015
• 页码: 114
• 关键词: LINEAR-REGRESSION; curve registration; Synchronization; splines; sample; models

摘要

A characteristic feature of functional data is the presence of phase variability in addition to amplitude variability. Existing functional regression methods do not handle time variability in an explicit and efficient way. In this paper we introduce a functional regression method that incorporates time warping as an intrinsic part of the model. The method achieves good predictive power in a parsimonious way and allows unified statistical inference about phase and amplitude components. The asymptotic distribution of the estimators is derived and their finite-sample properties are studied by simulation. An application involving ground-level ozone trajectories is presented.