Approximating fragmented functional data by segments of Markov chains

• 成果类型: Article
• 署名作者: Delaigle, A.; Hall, P.
• 署名单位: University of Melbourne
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asw040
• 发表日期: 2016
• 页码: 779799
• 关键词: LINEAR-REGRESSION; prediction; PERSPECTIVE; density; FLOWS; MODEL

摘要

We consider curve extension and linear prediction for functional data observed only on a part of their domain, in the form of fragments. We suggest an approach based on a combination of Markov chains and nonparametric smoothing techniques, which enables us to extend the observed fragments and construct approximated prediction intervals around them, construct mean and covariance function estimators, and derive a linear predictor. The procedure is illustrated on real and simulated data.