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一个稀疏的正规均值模型的测试和估计,与形状约束推理的连接

Testing and estimation in a sparse normal means model, with connections to shape restricted inference
课程网址: http://videolectures.net/smls09_wellner_taeiasn/  
主讲教师: Jon Wellner
开课单位: 华盛顿大学
开课时间: 2009-05-06
课程语种: 英语
中文简介:
Donoho和Jin(2004)根据Inster(1999)的工作,研究了稀疏正态均值模型中任何信号的测试问题,发现存在一个“检测边界”,在该边界上可以检测到信号,在该边界下没有任何测试电源。他们表明,图基的“更高批评”统计数据达到了检测界限。我将介绍一个新的测试统计系列,它基于由S∈[−1,2]索引的phi发散,所有这些都实现了Donoho Jin-ingster检测边界。我还将回顾最近关于估计非零均值比例的工作,并将其与形状约束估计联系起来。
课程简介: Donoho and Jin (2004), following work of Ingster (1999), studied the problem of testing for any signal in a sparse normal means model and showed that there is a “detection boundary” above which the signal can be detected and below which no test has any power. They showed that Tukey’s “higher criticism” statistic achieves the detection boundary. I will introduce a new family of test statistics based on phi-divergences indexed by s ∈ [−1, 2] which all achieve the Donoho-Jin- Ingster detection boundary. I will also review recent work on estimating the proportion of non-zero means and make some connections to shape-constrained estimation.
关 键 词: 模型; 稀疏; 估计
课程来源: 视频讲座网
最后编审: 2020-06-03:魏雪琼(课程编辑志愿者)
阅读次数: 37