A Comparison of Hierarchical Bayesian Models for Small Area Estimation of Counts
作者: Matilde TrevisaniNicola Torelli
刊名: Open Journal of Statistics, 2017, Vol.07 (03), pp.521-550
来源数据库: Scientific Research Publishing Journal
DOI: 10.4236/ojs.2017.73036
原始语种摘要: Small area estimation (SAE) tackles the problem of providing reliable estimates for small areas, i.e. , subsets of the population for which sample information is not sufficient to warrant the use of a direct estimator. Hierarchical Bayesian approach to SAE problems offers several advantages over traditional SAE models including the ability of appropriately accounting for the type of surveyed variable. In this paper, a number of model specifications for estimating small area counts are discussed and their relative merits are illustrated. We conducted a simulation study by reproducing in a simplified form the Italian Labour Force Survey and taking the Local Labor Markets as target areas. Simulated data were generated by assuming population characteristics of interest as well as survey...
全文获取路径: PDF下载  Scientific Research Publishing  (合作)
分享到:

×
关键词翻译
关键词翻译
  • unemployment 失业
  • general 普遍的
  • nonnormal 非正规的
  • accounting 会计
  • ability 能力
  • estimating 价值估计
  • number 号码
  • stochastically 随机地
  • variable 变量
  • model 模型