{"title":"不同损失下长度偏置Logistic分布的贝叶斯风险分析","authors":"Ranjita Pandey, Pulkit Srivastava, Danish Ali","doi":"10.37398/jsr.2022.660333","DOIUrl":null,"url":null,"abstract":"The aim of this paper is parametric and reliability estimation for the two parameter length biased log-logistic distribution under squared error, generalized exponential, linear exponential and precautionary loss functions. Bayes estimates obtained under non informative priors through Lindleys approximation and through Markov Chain Monte Carlo are then compared with the classical parametric estimates. Bayesian risk analysis based on a simulated and a real data set are used to demonstrate application of the theoretic results.","PeriodicalId":16984,"journal":{"name":"JOURNAL OF SCIENTIFIC RESEARCH","volume":"28 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian Risk Analysis for Length Biased Log Logistic Distribution Under Different Loss\",\"authors\":\"Ranjita Pandey, Pulkit Srivastava, Danish Ali\",\"doi\":\"10.37398/jsr.2022.660333\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this paper is parametric and reliability estimation for the two parameter length biased log-logistic distribution under squared error, generalized exponential, linear exponential and precautionary loss functions. Bayes estimates obtained under non informative priors through Lindleys approximation and through Markov Chain Monte Carlo are then compared with the classical parametric estimates. Bayesian risk analysis based on a simulated and a real data set are used to demonstrate application of the theoretic results.\",\"PeriodicalId\":16984,\"journal\":{\"name\":\"JOURNAL OF SCIENTIFIC RESEARCH\",\"volume\":\"28 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JOURNAL OF SCIENTIFIC RESEARCH\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37398/jsr.2022.660333\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JOURNAL OF SCIENTIFIC RESEARCH","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37398/jsr.2022.660333","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bayesian Risk Analysis for Length Biased Log Logistic Distribution Under Different Loss
The aim of this paper is parametric and reliability estimation for the two parameter length biased log-logistic distribution under squared error, generalized exponential, linear exponential and precautionary loss functions. Bayes estimates obtained under non informative priors through Lindleys approximation and through Markov Chain Monte Carlo are then compared with the classical parametric estimates. Bayesian risk analysis based on a simulated and a real data set are used to demonstrate application of the theoretic results.