4.7 Article

Covariance estimation error of incomplete functional data under RKHS framework

期刊

APPLIED MATHEMATICS AND COMPUTATION
卷 443, 期 -, 页码 -

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2022.127712

关键词

Reproducing kernel Hilbert space; Functional data fragment; Covariance estimation; N resolution patched method; Mean square integrability

向作者/读者索取更多资源

In recent years, functional data analysis (FDA) and reproducing kernel Hilbert space (RKHS) have been commonly used in various applications. However, the application of RKHS framework to study the covariance of incomplete functional data is rare. This paper investigates the global estimation error of the covariance function obtained from fragmented data, considering the connection between functional data and RKHS. The simulation results demonstrate the validity of the theoretical findings by comparing the estimation errors of two types of functional data.
In recent years, functional data analysis (FDA) and reproducing kernel Hilbert space (RKHS) are frequently encountered in various applications. However, employing the RKHS frame-work to study the covariance of incomplete functional data is rare. In this paper, we con-sider the global estimation error of the covariance function obtained by fragment data. Our theorem is built on the connection between functional data and RKHS, by using the covariance function as the reproducing kernel. We take the mean square integrability of functional data into consideration, and ease the previous restrictions of covariance func-tion and observation area. Simulation results show the veracity of our theoretical finding by comparing the error of two kinds of functional data in covariance estimation. The exist-ing N resolution patched (N-rp) method to estimate covariance in a local observation area has been improved, resulting in a considerable reduction in computing costs. (c) 2022 Elsevier Inc. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据