基于经验分布函数的多变量观测的多用途开放式监测程序

IF 1.2 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Time Series Analysis Pub Date : 2023-03-06 DOI:10.1111/jtsa.12683

Mark Holmes, Ivan Kojadinovic, Alex Verhoijsen

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引用次数: 0

摘要

我们提出了非参数开放式序列检验程序，该程序可以检测可能的多变量观测的当代分布函数的所有类型的变化。从理论上研究了它们在平稳性和可替代平稳性下的渐近性质。蒙特卡罗实验揭示了在连续单变量、二变量和三变量观测的情况下，它们良好的有限样本行为。一个简短的数据示例结束了这项工作。

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Multi-purpose open-end monitoring procedures for multivariate observations based on the empirical distribution function

We propose non-parametric open-end sequential testing procedures that can detect all types of changes in the contemporary distribution function of possibly multivariate observations. Their asymptotic properties are theoretically investigated under stationarity and under alternatives to stationarity. Monte Carlo experiments reveal their good finite-sample behavior in the case of continuous univariate, bivariate and trivariate observations. A short data example concludes the work.

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来源期刊

Journal of Time Series Analysis 数学-数学跨学科应用

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of biological data, control systems, signal processing and communications and vibrations engineering. The Journal of Time Series Analysis started in 1980, has since become the leading journal in its field, publishing papers on both fundamental theory and applications, as well as review papers dealing with recent advances in major areas of the subject and short communications on theoretical developments. The editorial board consists of many of the world''s leading experts in Time Series Analysis.