.png){kind=icon}
返回
OPTIMAL ADAPTIVE ESTIMATION OF LINEAR FUNCTIONALS UNDER SPARSITY
.png){kind=icon}
OA
AI
DOI：10.1214/17-AOS1653.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We consider the problem of estimation of a linear functional in the Gaussian sequence model where the unknown vector theta is an element of R-d belongs to a class of s-sparse vectors with unknown s. We suggest an adaptive estimator achieving a nonasymptotic rate of convergence that differs from the minimax rate at most by a logarithmic factor. We also show that this optimal adaptive rate cannot be improved when s is unknown. Furthermore, we address the issue of simultaneous adaptation to s and to the variance sigma(2) of the noise. We suggest an estimator that achieves the optimal adaptive rate when both s and sigma(2) are unknown.
Keyword:
Nonasymptotic minimax estimation
adaptive estimation
linear functional
sparsity
unknown noise variance
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.7
论文数:
2.8K
被引数:
2.9W
机构
查看更多机构
引用论文
MINIMAX ESTIMATION OF LINEAR AND QUADRATIC FUNCTIONALS ON SPARSITY CLASSES稀疏课程上线性和二次函数的MINIMAX估计.png)
ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏
没有更多内容