Search for: All records

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 $$j\notin S$$. 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.