Classification Using Censored Functional Data
成果类型：
Article
署名作者：
Delaigle, Aurore; Hall, Peter
署名单位：
University of Melbourne
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2013.824893
发表日期：
2013
页码：
1269-1283
关键词：
摘要：
We consider classification of functional data when the training curves are not observed on the same interval. Different types of classifier are suggested, one of which involves a new curve extension procedure. Our approach enables us to exploit the information contained in the endpoints of these intervals by incorporating it in an explicit but flexible way. We study asymptotic properties of our classifiers, and show that, in a variety of settings, they can even produce asymptotically perfect classification. The performance of our techniques is illustrated in applications to real and simulated data. Supplementary materials for this article are available online.