{"title":"随机脆弱性下的死亡率模型","authors":"Kazi Tanvir Hasan, Olcay Akman","doi":"10.35834/2021/3301105","DOIUrl":null,"url":null,"abstract":"When mortality statistics are reported for fatal diseases, they reflect the ratio of the overall mortality within the target population which is impacted from the disease in question. Reporting overall mortality leads to erroneous predictions since cohorts with different frailties are impacted at different rates. In this paper, we study methods for predicting mortality under varying conditions with the goal of removing the impact of hidden heterogeneity from the parameter estimates.","PeriodicalId":42784,"journal":{"name":"Missouri Journal of Mathematical Sciences","volume":" ","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mortality Modeling Under Stochastic Frailty\",\"authors\":\"Kazi Tanvir Hasan, Olcay Akman\",\"doi\":\"10.35834/2021/3301105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When mortality statistics are reported for fatal diseases, they reflect the ratio of the overall mortality within the target population which is impacted from the disease in question. Reporting overall mortality leads to erroneous predictions since cohorts with different frailties are impacted at different rates. In this paper, we study methods for predicting mortality under varying conditions with the goal of removing the impact of hidden heterogeneity from the parameter estimates.\",\"PeriodicalId\":42784,\"journal\":{\"name\":\"Missouri Journal of Mathematical Sciences\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2021-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Missouri Journal of Mathematical Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35834/2021/3301105\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Missouri Journal of Mathematical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35834/2021/3301105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS","Score":null,"Total":0}
When mortality statistics are reported for fatal diseases, they reflect the ratio of the overall mortality within the target population which is impacted from the disease in question. Reporting overall mortality leads to erroneous predictions since cohorts with different frailties are impacted at different rates. In this paper, we study methods for predicting mortality under varying conditions with the goal of removing the impact of hidden heterogeneity from the parameter estimates.
期刊介绍:
Missouri Journal of Mathematical Sciences (MJMS) publishes well-motivated original research articles as well as expository and survey articles of exceptional quality in mathematical sciences. A section of the MJMS is also devoted to interesting mathematical problems and solutions.