Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Statistical significance in high-dimensional linear mixed models   This paper concerns the development of an inferential framework for
high-dimensional linear mixed effect models. These are suitable models, for
instance, when we have $n$ repeated measurements for $M$ subjects. We consider
a scenario where the number of fixed effects $p$ is large (and may be larger
than $M$), but the number of random effects $q$ is small. Our framework is
inspired by a recent line of work that proposes de-biasing penalized estimators
to perform inference for high-dimensional linear models with fixed effects
only. In particular, we demonstrate how to correct a `naive' ridge estimator in
extension of work by B\"uhlmann (2013) to build asymptotically valid confidence
intervals for mixed effect models. We validate our theoretical results with
numerical experiments, in which we show our method outperforms those that fail
to account for correlation induced by the random effects. For a practical
demonstration we consider a riboflavin production dataset that exhibits group
structure, and show that conclusions drawn using our method are consistent with
those obtained on a similar dataset without group structure.

37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph