.png){kind=icon}
Return
Adaptive Sparse Estimation With Side Information
.png){kind=icon}
OA
AI
DOI：10.1080/01621459.2019.1679639.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The article considers the problem of estimating a high-dimensional sparse parameter in the presence of side information that encodes the sparsity structure. We develop a general framework that involves first using an auxiliary sequence to capture the side information, and then incorporating the auxiliary sequence in inference to reduce the estimation risk. The proposed method, which carries out adaptive Stein's unbiased risk estimate-thresholding using side information (ASUS), is shown to have robust performance and enjoy optimality properties. We develop new theories to characterize regimes in which ASUS far outperforms competitive shrinkage estimators, and establish precise conditions under which ASUS is asymptotically optimal. Simulation studies are conducted to show that ASUS substantially improves the performance of existing methods in many settings. The methodology is applied for analysis of data from single cell virology studies and microarray time course experiments. for this article are available online.
Keywords:
Adaptive shrinkage estimation
Higher order minimax risk
Inference with side information
Sparsity
SURE shrinkage
Two-sample inference
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3
Papers:
5.2K
Citations:
4.8W
Organization
Cited Papers
MINIMAX ESTIMATION OF LINEAR AND QUADRATIC FUNCTIONALS ON SPARSITY CLASSES.png)
ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSave
ShareSaveNONPARAMETRIC EMPIRICAL BAYES AND COMPOUND DECISION APPROACHES TO ESTIMATION OF A HIGH-DIMENSIONAL VECTOR OF NORMAL MEANSANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSaveShareSave
View more