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  • 标题:Hypothesis testing by convex optimization
  • 本地全文:下载
  • 作者:Alexander Goldenshluger ; Anatoli Juditsky ; Arkadi Nemirovski
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2015
  • 卷号:9
  • 期号:2
  • 页码:1645-1712
  • DOI:10.1214/15-EJS1054
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要:We discuss a general approach to hypothesis testing. The main “building block” of the proposed construction is a test for a pair of hypotheses in the situation where each particular hypothesis states that the vector of parameters identifying the distribution of observations belongs to a convex compact set associated with the hypothesis. This test, under appropriate assumptions, is nearly optimal and is yielded by a solution to a convex optimization problem, so that the construction admits computationally efficient implementation. We further demonstrate that our assumptions are satisfied in several important and interesting applications. Finally, we show how our approach can be applied to a rather general testing problems encompassing several classical statistical settings.
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