A nonparametric partially sequential test for early detection of newly emerging phenomena
dc.authorid | Kozan, Agah/0000-0002-7387-9701 | |
dc.authorid | Tanil, Halil/0000-0001-5402-8859 | |
dc.authorscopusid | 16311149100 | |
dc.authorscopusid | 57212553095 | |
dc.authorwosid | TANIL, Halil/ABH-1691-2021 | |
dc.authorwosid | TANIL, Halil/AAG-6578-2021 | |
dc.authorwosid | Kozan, Agah/AAA-5679-2020 | |
dc.contributor.author | Tanil, Halil | |
dc.contributor.author | Kozan, Agah | |
dc.date.accessioned | 2023-01-12T19:59:02Z | |
dc.date.available | 2023-01-12T19:59:02Z | |
dc.date.issued | 2022 | |
dc.department | N/A/Department | en_US |
dc.description.abstract | An event encountered for the first time can become a common phenomenon in a population through transmission over time. This study focuses on nonparametric partially sequential test statistics in order to early detect such events. For this purpose, firstly, the partially sequential test statistic given in Mukherjee [1] is considered. Then, with a modification of this statistic, a new nonparametric partially sequential test statistic that works at almost the desired alpha level of significance is revealed. Also, based on Monte-Carlo simulations, the proposed test statistic is shown to be more powerful than Mukherjee's statistic. Lastly, two illustrative numerical examples are also given. | en_US |
dc.identifier.doi | 10.1080/00949655.2021.1998499 | |
dc.identifier.endpage | 1437 | en_US |
dc.identifier.issn | 0094-9655 | |
dc.identifier.issn | 1563-5163 | |
dc.identifier.issn | 0094-9655 | en_US |
dc.identifier.issn | 1563-5163 | en_US |
dc.identifier.issue | 7 | en_US |
dc.identifier.scopus | 2-s2.0-85119200449 | en_US |
dc.identifier.scopusquality | Q2 | en_US |
dc.identifier.startpage | 1426 | en_US |
dc.identifier.uri | https://doi.org/10.1080/00949655.2021.1998499 | |
dc.identifier.uri | https://hdl.handle.net/11454/77081 | |
dc.identifier.volume | 92 | en_US |
dc.identifier.wos | WOS:000717358300001 | en_US |
dc.identifier.wosquality | Q3 | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis Ltd | en_US |
dc.relation.ispartof | Journal Of Statistical Computation And Simulation | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Partially sequential tests | en_US |
dc.subject | Sequential ranks | en_US |
dc.subject | Emerging phenomenon | en_US |
dc.subject | Exponential growth | en_US |
dc.subject | Early detection | en_US |
dc.subject | Statistical power | en_US |
dc.subject | 2-Sample Test | en_US |
dc.subject | Trend | en_US |
dc.subject | Illustration | en_US |
dc.subject | Location | en_US |
dc.title | A nonparametric partially sequential test for early detection of newly emerging phenomena | en_US |
dc.type | Article | en_US |