Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
1
The categories of this paper are: cs
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom