Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable simultaneous inference in high-dimensional linear regression
  models   The computational complexity of simultaneous inference methods in
high-dimensional linear regression models quickly increases with the number
variables. This paper proposes a computationally efficient method based on the
Moore-Penrose pseudoinverse. Under a symmetry assumption on the available
regressors, the estimators are normally distributed and accompanied by a
closed-form expression for the standard errors that is free of tuning
parameters. We study the numerical performance in Monte Carlo experiments that
mimic the size of modern applications for which existing methods are
computationally infeasible. We find close to nominal coverage, even in settings
where the imposed symmetry assumption does not hold. Regularization of the
pseudoinverse via a ridge adjustment is shown to yield possible efficiency
gains.

7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
1
The categories of this paper are: cs
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph