{"title":"关于三参数威布尔分布形状参数估计","authors":"M. Teimouri, Arjun K. Gupta","doi":"10.6339/JDS.2013.11(3).1110","DOIUrl":null,"url":null,"abstract":"The Weibull distribution has received much interest in reliability theory. The well-known maximum likelihood estimators (MLE) of this fam- ily are not available in closed form expression. In this work, we propose a consistent and closed form estimator for shape parameter of three-parameter Weibull distribution. Apart from high degree of performance, the derived estimator is location and scale-invariant.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":"{\"title\":\"On the Three-Parameter Weibull Distribution Shape Parameter Estimation\",\"authors\":\"M. Teimouri, Arjun K. Gupta\",\"doi\":\"10.6339/JDS.2013.11(3).1110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Weibull distribution has received much interest in reliability theory. The well-known maximum likelihood estimators (MLE) of this fam- ily are not available in closed form expression. In this work, we propose a consistent and closed form estimator for shape parameter of three-parameter Weibull distribution. Apart from high degree of performance, the derived estimator is location and scale-invariant.\",\"PeriodicalId\":73699,\"journal\":{\"name\":\"Journal of data science : JDS\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"38\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of data science : JDS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.6339/JDS.2013.11(3).1110\",\"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 data science : JDS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.6339/JDS.2013.11(3).1110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the Three-Parameter Weibull Distribution Shape Parameter Estimation
The Weibull distribution has received much interest in reliability theory. The well-known maximum likelihood estimators (MLE) of this fam- ily are not available in closed form expression. In this work, we propose a consistent and closed form estimator for shape parameter of three-parameter Weibull distribution. Apart from high degree of performance, the derived estimator is location and scale-invariant.