{"title":"Using the growth curve model in classification of repeated measurements","authors":"Dietrich von Rosen, Martin Singull","doi":"10.1007/s10463-024-00900-1","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, discrimination between two populations following the growth curve model is considered. A likelihood-based classification procedure is established, in the sense that we compare the two likelihoods given that the new observation belongs to respective population. The possibility to classify the new observation as belonging to an unknown population is discussed, which is shown to be natural when considering growth curves. Several examples and simulations are given to emphasize this possibility.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"76 3","pages":"511 - 534"},"PeriodicalIF":0.6000,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of the Institute of Statistical Mathematics","FirstCategoryId":"100","ListUrlMain":"https://link.springer.com/article/10.1007/s10463-024-00900-1","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, discrimination between two populations following the growth curve model is considered. A likelihood-based classification procedure is established, in the sense that we compare the two likelihoods given that the new observation belongs to respective population. The possibility to classify the new observation as belonging to an unknown population is discussed, which is shown to be natural when considering growth curves. Several examples and simulations are given to emphasize this possibility.
期刊介绍：
Annals of the Institute of Statistical Mathematics (AISM) aims to provide a forum for open communication among statisticians, and to contribute to the advancement of statistics as a science to enable humans to handle information in order to cope with uncertainties. It publishes high-quality papers that shed new light on the theoretical, computational and/or methodological aspects of statistical science. Emphasis is placed on (a) development of new methodologies motivated by real data, (b) development of unifying theories, and (c) analysis and improvement of existing methodologies and theories.
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