{"title":"参数脆弱性模型中的Lasso估计","authors":"Anu Sirohi, Prem Shenkar Jha","doi":"10.47974/jios-1291","DOIUrl":null,"url":null,"abstract":"This paper proposed lasso estimator in parametric frailty model. Comparison of lasso (least absolute shrinkage and selection operator) and maximum likelihood (ML) estimator is done in terms of scalar mean square error (MSE). Performance of lasso estimator is examined through simulation study. Furthermore, approach is applied to analyze infant mortality in India.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lasso estimation in parametric frailty model\",\"authors\":\"Anu Sirohi, Prem Shenkar Jha\",\"doi\":\"10.47974/jios-1291\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposed lasso estimator in parametric frailty model. Comparison of lasso (least absolute shrinkage and selection operator) and maximum likelihood (ML) estimator is done in terms of scalar mean square error (MSE). Performance of lasso estimator is examined through simulation study. Furthermore, approach is applied to analyze infant mortality in India.\",\"PeriodicalId\":46518,\"journal\":{\"name\":\"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47974/jios-1291\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47974/jios-1291","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
This paper proposed lasso estimator in parametric frailty model. Comparison of lasso (least absolute shrinkage and selection operator) and maximum likelihood (ML) estimator is done in terms of scalar mean square error (MSE). Performance of lasso estimator is examined through simulation study. Furthermore, approach is applied to analyze infant mortality in India.