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  • 标题:A Generalised Semiparametric Bayesian Fay–Herriot Model for Small Area Estimation Shrinking Both Means and Variances
  • 本地全文:下载
  • 作者:Silvia Polettini
  • 期刊名称:Bayesian Analysis
  • 印刷版ISSN:1931-6690
  • 电子版ISSN:1936-0975
  • 出版年度:2017
  • 卷号:12
  • 期号:3
  • 页码:729-752
  • DOI:10.1214/16-BA1019
  • 语种:English
  • 出版社:International Society for Bayesian Analysis
  • 摘要:In survey sampling, interest often lies in unplanned domains (or small areas), whose sample sizes may be too small to allow for accurate design-based inference. To improve the direct estimates by borrowing strength from similar domains, most small area methods rely on mixed effects regression models.
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