The Annals of Statistics
- Ann. Statist.
- Volume 42, Number 3 (2014), 1166-1202.
On asymptotically optimal confidence regions and tests for high-dimensional models
We propose a general method for constructing confidence intervals and statistical tests for single or low-dimensional components of a large parameter vector in a high-dimensional model. It can be easily adjusted for multiplicity taking dependence among tests into account. For linear models, our method is essentially the same as in Zhang and Zhang [J. R. Stat. Soc. Ser. B Stat. Methodol. 76 (2014) 217–242]: we analyze its asymptotic properties and establish its asymptotic optimality in terms of semiparametric efficiency. Our method naturally extends to generalized linear models with convex loss functions. We develop the corresponding theory which includes a careful analysis for Gaussian, sub-Gaussian and bounded correlated designs.
Ann. Statist. Volume 42, Number 3 (2014), 1166-1202.
First available in Project Euclid: 20 June 2014
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van de Geer, Sara; Bühlmann, Peter; Ritov, Ya’acov; Dezeure, Ruben. On asymptotically optimal confidence regions and tests for high-dimensional models. Ann. Statist. 42 (2014), no. 3, 1166--1202. doi:10.1214/14-AOS1221. https://projecteuclid.org/euclid.aos/1403276911.
- Supplementary material: Supplement to “On asymptotically optimal confidence regions and tests for high-dimensional models”. The supplemental article contains additional empirical results, as well as the proofs of Theorems 2.3 and 3.2, Lemmas 2.1 and 3.1.