{"title":"Sequential hypothesis testing for selecting the number of changepoints in segmented regression models","authors":"","doi":"10.1007/s10651-024-00605-x","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Segmented regression is widely used in many disciplines, especially when dealing with environmental data. This paper deals with the problem of selecting the correct number of changepoints in segmented regression models. A review of the usual selection criteria, namely information criteria and hypothesis testing, is provided. We enhance the latter method by proposing a novel sequential hypothesis testing procedure to address this problem. Our sequential procedure’s performance is compared to methods based on information-based criteria through simulation studies. The results show that our proposal performs similarly to its competitors for the Gaussian, Binomial, and Poisson cases. Finally, we present two applications to environmental datasets of crime data in Valencia and global temperature land data.</p>","PeriodicalId":50519,"journal":{"name":"Environmental and Ecological Statistics","volume":"303 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental and Ecological Statistics","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s10651-024-00605-x","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Segmented regression is widely used in many disciplines, especially when dealing with environmental data. This paper deals with the problem of selecting the correct number of changepoints in segmented regression models. A review of the usual selection criteria, namely information criteria and hypothesis testing, is provided. We enhance the latter method by proposing a novel sequential hypothesis testing procedure to address this problem. Our sequential procedure’s performance is compared to methods based on information-based criteria through simulation studies. The results show that our proposal performs similarly to its competitors for the Gaussian, Binomial, and Poisson cases. Finally, we present two applications to environmental datasets of crime data in Valencia and global temperature land data.
期刊介绍：
Environmental and Ecological Statistics publishes papers on practical applications of statistics and related quantitative methods to environmental science addressing contemporary issues.
Emphasis is on applied mathematical statistics, statistical methodology, and data interpretation and improvement for future use, with a view to advance statistics for environment, ecology and environmental health, and to advance environmental theory and practice using valid statistics.
Besides clarity of exposition, a single most important criterion for publication is the appropriateness of the statistical method to the particular environmental problem. The Journal covers all aspects of the collection, analysis, presentation and interpretation of environmental data for research, policy and regulation. The Journal is cross-disciplinary within the context of contemporary environmental issues and the associated statistical tools, concepts and methods. The Journal broadly covers theory and methods, case studies and applications, environmental change and statistical ecology, environmental health statistics and stochastics, and related areas. Special features include invited discussion papers; research communications; technical notes and consultation corner; mini-reviews; letters to the Editor; news, views and announcements; hardware and software reviews; data management etc.
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