{"title":"关于选取包含k个总体中t个最优的子集:尺度参数情况","authors":"J. B. Ofosu","doi":"10.5109/13090","DOIUrl":null,"url":null,"abstract":"The problem of selecting a subset of fixed size s which includes the t best of k populations (t s < k), based on a pre-determined sample size n from each of the k populations, is studied as a multiple decision problem. It is assumed that the bestness of a population is characterized by its scale parameter ; the best population being the one with the largest scale parameter, and so on. Exact small and large sample methods of finding n are given for the scale parameter problem for ( i) Gamma distributions with known (possibly unequal) shape parameters (ii) Weibull distributions with known shape parameters. Some tables computed by these methods are provided. A dual problem is also discussed.","PeriodicalId":287765,"journal":{"name":"Bulletin of Mathematical Statistics","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1975-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ON SELECTING A SUBSET WHICH INCLUDES THE $ t $ BEST OF $ k $ POPULATIONS : SCALE PARAMETER CASE\",\"authors\":\"J. B. Ofosu\",\"doi\":\"10.5109/13090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of selecting a subset of fixed size s which includes the t best of k populations (t s < k), based on a pre-determined sample size n from each of the k populations, is studied as a multiple decision problem. It is assumed that the bestness of a population is characterized by its scale parameter ; the best population being the one with the largest scale parameter, and so on. Exact small and large sample methods of finding n are given for the scale parameter problem for ( i) Gamma distributions with known (possibly unequal) shape parameters (ii) Weibull distributions with known shape parameters. Some tables computed by these methods are provided. A dual problem is also discussed.\",\"PeriodicalId\":287765,\"journal\":{\"name\":\"Bulletin of Mathematical Statistics\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1975-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bulletin of Mathematical Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5109/13090\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of Mathematical Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5109/13090","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
基于预先确定的k个总体的样本量n,选择包含k个总体(t s < k)中的t个最优的固定大小的子集s的问题,作为一个多重决策问题进行研究。假设种群的最优性由其尺度参数表征;最佳种群是具有最大尺度参数的种群,以此类推。对于(i)具有已知(可能不相等)形状参数的Gamma分布(ii)具有已知形状参数的Weibull分布,给出了确定n的精确小样本和大样本方法。给出了用这些方法计算的一些表。讨论了一个对偶问题。
ON SELECTING A SUBSET WHICH INCLUDES THE $ t $ BEST OF $ k $ POPULATIONS : SCALE PARAMETER CASE
The problem of selecting a subset of fixed size s which includes the t best of k populations (t s < k), based on a pre-determined sample size n from each of the k populations, is studied as a multiple decision problem. It is assumed that the bestness of a population is characterized by its scale parameter ; the best population being the one with the largest scale parameter, and so on. Exact small and large sample methods of finding n are given for the scale parameter problem for ( i) Gamma distributions with known (possibly unequal) shape parameters (ii) Weibull distributions with known shape parameters. Some tables computed by these methods are provided. A dual problem is also discussed.