Test for the mean of high-dimensional functional time series

IF 1.5 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computational Statistics & Data Analysis Pub Date : 2024-08-22 DOI:10.1016/j.csda.2024.108040

引用次数: 0

Abstract

The one-sample test and two-sample test for the mean of high-dimensional functional time series are considered in this study. The proposed tests are built on the dimension-wise max-norm of the sum of squares of diverging projections. The null distribution of the test statistics is investigated using normal approximation, and the asymptotic behavior under the alternative is studied. The approach is robust to the cross-series dependence of unknown forms and magnitude. To approximate the critical values, a blockwise wild bootstrap method for functional time series is employed. Both fully and partially observed data are analyzed in theoretical research and numerical studies. Evidence from simulation studies and an IT stock data case study demonstrates the usefulness of the test in practice. The proposed methods have been implemented in a R package.

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高维函数时间序列均值检验

本研究考虑了高维函数时间序列均值的单样本检验和双样本检验。提出的检验建立在发散投影平方和的维度最大正值基础上。使用正态近似法研究了检验统计量的零分布，并研究了备选方案下的渐近行为。该方法对未知形式和幅度的跨序列依赖性具有鲁棒性。为了近似临界值，采用了功能时间序列的 blockwise wild bootstrap 方法。在理论研究和数值研究中，对完全观测数据和部分观测数据都进行了分析。来自模拟研究和 IT 股票数据案例研究的证据证明了该检验方法在实践中的实用性。所提出的方法已在 R 软件包中实现。

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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来源期刊

Computational Statistics & Data Analysis 数学-计算机：跨学科应用

CiteScore

3.70

自引率

5.60%

发文量

167

审稿时长

60 days

期刊介绍： Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]

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