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  • 标题:INFERENTIAL ISSUES IN MODEL-BASED SMALL AREA ESTIMATION: SOME NEW DEVELOPMENTS
  • 本地全文:下载
  • 作者:J. N. K. Rao
  • 期刊名称:Statistics in Transition
  • 印刷版ISSN:1234-7655
  • 电子版ISSN:2450-0291
  • 出版年度:2015
  • 卷号:16
  • 期号:4
  • 页码:491-510
  • DOI:10.21307/stattrans-2015-029
  • 出版社:Exeley Inc.
  • 摘要:Small area estimation (SAE) has seen a rapid growth over the past 10 years or so. Earlier work is covered in the author's book (Rao 2003). The main purpose of this paper is to highlight some new developments in model-based SAE since the publication of the author's book. A large part of the new theory addressed practical issues associated with the model-based approach, and we present some of those methods for area level and unit level models. We also briefly mention some new work on synthetic estimation of area means or totals based on implicit models.
  • 关键词:area level models;complex parameters;informative sampling;model misspecification;robust estimation;unit level models.
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