{"title":"An algorithm for a new method of change-point analysis in the independent Poisson sequence","authors":"C. Hirotsu, H. Tsuruta","doi":"10.1515/bile-2017-0001","DOIUrl":null,"url":null,"abstract":"Summary Step change-point and slope change-point models in the independent Poisson sequence are developed based on accumulated and doubly-accumulated statistics. The method for the step change-point model developed in Section 2 is an alternative to the likelihood ratio test of Worsley (1986) and the algorithm for p-value calculation based on the first-order Markov property is the same as that given there. Different algorithms for the non-null distribution and inference on the change-point itself are, however, newly developed and a Pascal program is given in the Appendix. These methods are extended to the slope change-point model in Section 3. The approach is essentially the same as that of Section 2 but the algorithm is now based on the second-order Markov property and becomes a little more complicated. The Pascal program related to the slope change-point model is supported on the website, URL: https://corec.meisei-u.ac.jp/labs/hirotsu/.","PeriodicalId":8933,"journal":{"name":"Biometrical Letters","volume":"28 1","pages":"1 - 24"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biometrical Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/bile-2017-0001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Summary Step change-point and slope change-point models in the independent Poisson sequence are developed based on accumulated and doubly-accumulated statistics. The method for the step change-point model developed in Section 2 is an alternative to the likelihood ratio test of Worsley (1986) and the algorithm for p-value calculation based on the first-order Markov property is the same as that given there. Different algorithms for the non-null distribution and inference on the change-point itself are, however, newly developed and a Pascal program is given in the Appendix. These methods are extended to the slope change-point model in Section 3. The approach is essentially the same as that of Section 2 but the algorithm is now based on the second-order Markov property and becomes a little more complicated. The Pascal program related to the slope change-point model is supported on the website, URL: https://corec.meisei-u.ac.jp/labs/hirotsu/.