.png){kind=icon}
返回
MULTISCALE BLIND SOURCE SEPARATION
.png){kind=icon}
OA
AI
DOI：10.1214/17-AOS1565.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We provide a new methodology for statistical recovery of single linear mixtures of piecewise constant signals (sources) with unknown mixing weights and change points in a multiscale fashion. We show exact recovery within an epsilon-neighborhood of the mixture when the sources take only values in a known finite alphabet. Based on this we provide the SLAM (Separates Linear Alphabet Mixtures) estimators for the mixing weights and sources. For Gaussian error, we obtain uniform confidence sets and optimal rates (up to log-factors) for all quantities. SLAM is efficiently computed as a nonconvex optimization problem by a dynamic program tailored to the finite alphabet assumption. Its performance is investigated in a simulation study. Finally, it is applied to assign copy-number aberrations from genetic sequencing data to different clones and to estimate their proportions.
Keyword:
Multiscale inference
honest confidence sets
change point regression
finite alphabet linear mixture
exact recovery
genetic sequencing
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
引用论文
分享收藏Estimating and testing linear models with multiple structural changes估计和测试具有多个结构变化的线性模型.png)
ECONOMETRICAOAAI
ECONOMETRICAIF7.1
OAAI分享收藏
查看更多内容