协整函数时间序列的函数主成分分析

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-06-23 DOI:10.1111/jtsa.12707

Won-Ki Seo

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引用次数: 0

摘要

函数主成分分析（FPCA）在函数时间序列（FTS）分析的发展中发挥了重要作用。本文研究了如何使用FPCA来分析协积分函数时间序列，并提出了一种新的统计工具FPCA的改进方案。我们改进的FPCA不仅提供了协积分向量的渐近更有效的估计量，而且还提出了新的KPSS型检验，用于检验协积分时间序列的一些基本性质。作为实证说明，我们的方法被应用于对数收益密度的时间序列。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Functional principal component analysis for cointegrated functional time series

Functional principal component analysis (FPCA) has played an important role in the development of functional time series analysis. This note investigates how FPCA can be used to analyze cointegrated functional time series and proposes a modification of FPCA as a novel statistical tool. Our modified FPCA not only provides an asymptotically more efficient estimator of the cointegrating vectors, but also leads to novel FPCA-based tests for examining essential properties of cointegrated functional time series.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.