{"title":"关于小面积比例的推论。","authors":"Shijie Chen, P Lahiri","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Design-based methods are generally inefficient for making inferences about small area proportions for rare events. In this paper, we discuss an alternative hierarchical model and the associated hierarchical Bayes methodology. Sufficient conditions for propriety of the posterior distributions of relevant parameters are presented.</p>","PeriodicalId":89431,"journal":{"name":"Journal of the Indian Society of Agricultural Statistics. Indian Society of Agricultural Statistics","volume":"66 1","pages":"121-124"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896051/pdf/nihms535133.pdf","citationCount":"0","resultStr":"{\"title\":\"Inferences on Small Area Proportions.\",\"authors\":\"Shijie Chen, P Lahiri\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Design-based methods are generally inefficient for making inferences about small area proportions for rare events. In this paper, we discuss an alternative hierarchical model and the associated hierarchical Bayes methodology. Sufficient conditions for propriety of the posterior distributions of relevant parameters are presented.</p>\",\"PeriodicalId\":89431,\"journal\":{\"name\":\"Journal of the Indian Society of Agricultural Statistics. Indian Society of Agricultural Statistics\",\"volume\":\"66 1\",\"pages\":\"121-124\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896051/pdf/nihms535133.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Indian Society of Agricultural Statistics. Indian Society of Agricultural Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"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 the Indian Society of Agricultural Statistics. Indian Society of Agricultural Statistics","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design-based methods are generally inefficient for making inferences about small area proportions for rare events. In this paper, we discuss an alternative hierarchical model and the associated hierarchical Bayes methodology. Sufficient conditions for propriety of the posterior distributions of relevant parameters are presented.