Sparse confidence sets for normal mean models
Abstract
In this paper, we propose a new framework to construct confidence sets for a $d$-dimensional unknown sparse parameter ${\boldsymbol \theta }$ under the normal mean model ${\boldsymbol X}\sim N({\boldsymbol \theta },\sigma ^{2}\bf{I})$. A key feature of the proposed confidence set is its capability to account for the sparsity of ${\boldsymbol \theta }$, thus named as sparse confidence set. This is in sharp contrast with the classical methods, such as the Bonferroni confidence intervals and other resampling-based procedures, where the sparsity of ${\boldsymbol \theta }$ is often ignored. Specifically, we require the desired sparse confidence set to satisfy the following two conditions: (i) uniformly over the parameter space, the coverage probability for ${\boldsymbol \theta }$ is above a pre-specified level; (ii) there exists a random subset $S$ of $\{1,...,d\}$ such that $S$ guarantees the pre-specified true negative rate for detecting non-zero $\theta _{j}$’s. To exploit the sparsity of ${\boldsymbol \theta }$, we allow the confidence interval for $\theta _{j}$ to degenerate to a single point 0 for any $j otin S$. Under this new framework, we first consider whether there exist sparse confidence sets that satisfy the above two conditions. To address this question, we establish a non-asymptotic minimax lower bound for the non-coverage probability over a suitable class of sparse confidence sets. The lower bound deciphers the role of sparsity and minimum signal-to-noise ratio (SNR) in the construction of sparse confidence sets. Furthermore, under suitable conditions on the SNR, a two-stage procedure is proposed to construct a sparse confidence set. To evaluate the optimality, the proposed sparse confidence set is shown to attain a minimax lower bound of some properly defined risk function up to a constant factor. Finally, we develop an adaptive procedure to the unknown sparsity. Numerical studies are conducted to verify the theoretical results.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 20, 2023
- Source ID
- 10.1093/imaiai/iaad003
Entities
People
- Guang Cheng
- Yang Ning
Organizations
- Cornell University
- National Science Foundation
- Office of Naval Research
- University of California