{"title":"基于MCMC方法的威布尔回归模型及其在可靠性中的应用","authors":"Jing Lin, Yuqi Han, Huiming Zhu, Jie Chen","doi":"10.3969/J.ISSN.1004-731X.2006.05.020","DOIUrl":null,"url":null,"abstract":"Weibull regression model was discussed,which is used widely in the family of Bayesian accelerated failure-time models.As for the productions whose life distributions belong to Weibull distribution,the MCMC method was brought forward based on Gibbs sampling to simulate dynamically the Markov Chain of the parameters' posterior distribution.From this,the parameters' Bayesian estimation of the Weibull regression model was given in the condition of the random truncated test and when the prior distribution of the failure rate belonged to the Gamma distribution,which improved the precision of the numeration.Also the data's simulation was utilized to show the process of setting the model by using the BUGS package.It proves the objectivity and validity of the model.","PeriodicalId":66490,"journal":{"name":"计算机仿真","volume":"1 1","pages":"1161-1163"},"PeriodicalIF":0.0000,"publicationDate":"2006-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Weibull Regression Model Based on MCMC Method and Its Application in Reliability\",\"authors\":\"Jing Lin, Yuqi Han, Huiming Zhu, Jie Chen\",\"doi\":\"10.3969/J.ISSN.1004-731X.2006.05.020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Weibull regression model was discussed,which is used widely in the family of Bayesian accelerated failure-time models.As for the productions whose life distributions belong to Weibull distribution,the MCMC method was brought forward based on Gibbs sampling to simulate dynamically the Markov Chain of the parameters' posterior distribution.From this,the parameters' Bayesian estimation of the Weibull regression model was given in the condition of the random truncated test and when the prior distribution of the failure rate belonged to the Gamma distribution,which improved the precision of the numeration.Also the data's simulation was utilized to show the process of setting the model by using the BUGS package.It proves the objectivity and validity of the model.\",\"PeriodicalId\":66490,\"journal\":{\"name\":\"计算机仿真\",\"volume\":\"1 1\",\"pages\":\"1161-1163\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"计算机仿真\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.3969/J.ISSN.1004-731X.2006.05.020\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"计算机仿真","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.3969/J.ISSN.1004-731X.2006.05.020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Weibull Regression Model Based on MCMC Method and Its Application in Reliability
Weibull regression model was discussed,which is used widely in the family of Bayesian accelerated failure-time models.As for the productions whose life distributions belong to Weibull distribution,the MCMC method was brought forward based on Gibbs sampling to simulate dynamically the Markov Chain of the parameters' posterior distribution.From this,the parameters' Bayesian estimation of the Weibull regression model was given in the condition of the random truncated test and when the prior distribution of the failure rate belonged to the Gamma distribution,which improved the precision of the numeration.Also the data's simulation was utilized to show the process of setting the model by using the BUGS package.It proves the objectivity and validity of the model.