{"title":"重新参数化柔性威布尔分布及其应用","authors":"F. Prataviera","doi":"10.1080/01966324.2021.1957731","DOIUrl":null,"url":null,"abstract":"Abstract A reparameterized flexible Weibull distribution indexed by median and a shape parameter is proposed for the development of regression models which includes the possibility of censored data. The reparameterization permits a straightforward interpretation of the regression coefficients in terms of the median. Model estimation is implemented via Classical and Bayesian approaches, and Monte Carlo simulations are carried out in order to evaluate the estimators performances for finite samples. In addition, the model is applied to three real data sets.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Reparameterized Flexible Weibull Distribution with Some Applications\",\"authors\":\"F. Prataviera\",\"doi\":\"10.1080/01966324.2021.1957731\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract A reparameterized flexible Weibull distribution indexed by median and a shape parameter is proposed for the development of regression models which includes the possibility of censored data. The reparameterization permits a straightforward interpretation of the regression coefficients in terms of the median. Model estimation is implemented via Classical and Bayesian approaches, and Monte Carlo simulations are carried out in order to evaluate the estimators performances for finite samples. In addition, the model is applied to three real data sets.\",\"PeriodicalId\":35850,\"journal\":{\"name\":\"American Journal of Mathematical and Management Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Journal of Mathematical and Management Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/01966324.2021.1957731\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Business, Management and Accounting\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Mathematical and Management Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/01966324.2021.1957731","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Business, Management and Accounting","Score":null,"Total":0}
Reparameterized Flexible Weibull Distribution with Some Applications
Abstract A reparameterized flexible Weibull distribution indexed by median and a shape parameter is proposed for the development of regression models which includes the possibility of censored data. The reparameterization permits a straightforward interpretation of the regression coefficients in terms of the median. Model estimation is implemented via Classical and Bayesian approaches, and Monte Carlo simulations are carried out in order to evaluate the estimators performances for finite samples. In addition, the model is applied to three real data sets.
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