实时变化点检测在线性模型中使用排序选择程序

IF 0.6 4区数学 Q4 STATISTICS & PROBABILITY

Sequential Analysis-Design Methods and Applications Pub Date : 2023-04-03 DOI:10.1080/07474946.2023.2187416

Chao Gu, Suthakaran Ratnasingam

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引用次数: 0

摘要

摘要我们提出了一种新的线性模型中的序列变化点检测方法。我们的方法使用给定的历史数据集来确定预变化模型。使用排序程序选择重要特征，这是一种创新方法，旨在揭示所有特征对模型影响的排序。我们建立了零假设和替代假设下检验统计量的渐近性质。通过仿真验证了该方法的性能。最后，我们用一个实际的数据应用来说明检测过程。

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Real-time change point detection in linear models using the ranking selection procedure

Abstract We propose a novel sequential change point detection method in linear models. Our method uses a given historical data set to determine the prechange model. Significant features are selected using the ranking procedure, which is an innovative approach aimed at revealing the rank of all features in terms of their effects on the model. We establish the asymptotic properties of the test statistic under the null and alternative hypotheses. Simulations are conducted to illustrate the performance of the proposed method. We conclude with a real data application to illustrate the detection procedure.

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来源期刊

Sequential Analysis-Design Methods and Applications STATISTICS & PROBABILITY-

CiteScore

1.40

自引率

12.50%

发文量

期刊介绍： The purpose of Sequential Analysis is to contribute to theoretical and applied aspects of sequential methodologies in all areas of statistical science. Published papers highlight the development of new and important sequential approaches. Interdisciplinary articles that emphasize the methodology of practical value to applied researchers and statistical consultants are highly encouraged. Papers that cover contemporary areas of applications including animal abundance, bioequivalence, communication science, computer simulations, data mining, directional data, disease mapping, environmental sampling, genome, imaging, microarrays, networking, parallel processing, pest management, sonar detection, spatial statistics, tracking, and engineering are deemed especially important. Of particular value are expository review articles that critically synthesize broad-based statistical issues. Papers on case-studies are also considered. All papers are refereed.