Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Penalized Inference with Direct False Discovery Rate Control in
  High-Dimensions   We propose a new method, semi-penalized inference with direct false discovery
rate control (SPIDR), for variable selection and confidence interval
construction in high-dimensional linear regression. SPIDR first uses a
semi-penalized approach to constructing estimators of the regression
coefficients. We show that the SPIDR estimator is ideal in the sense that it
equals an ideal least squares estimator with high probability under a sparsity
and other suitable conditions. Consequently, the SPIDR estimator is
asymptotically normal. Based on this distributional result, SPIDR determines
the selection rule by directly controlling false discovery rate. This provides
an explicit assessment of the selection error. This also naturally leads to
confidence intervals for the selected coefficients with a proper confidence
statement. We conduct simulation studies to evaluate its finite sample
performance and demonstrate its application on a breast cancer gene expression
data set. Our simulation studies and data example suggest that SPIDR is a
useful method for high-dimensional statistical inference in practice.

37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg