Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variable Selection for Additive Partial Linear Quantile Regression with
  Missing Covariates   The standard quantile regression model assumes a linear relationship at the
quantile of interest and that all variables are observed. We relax these
assumptions by considering a partial linear model while allowing for missing
linear covariates. To handle the potential bias caused by missing data we
propose a weighted objective function using inverse probability weighting. Our
work examines estimators using parametric and nonparametric estimates of the
missing probability. For variable selection of the linear terms in the presence
of missing data we consider a penalized and weighted objective function using
the non-convex penalties MCP or SCAD. Under standard conditions we demonstrate
that the penalized estimator has the oracle property including cases where
$p>>n$. Theoretical challenges include handling missing data and partial linear
models while working with a nonsmooth loss function and a non-convex penalty
function. The performance of the method is evaluated using Monte Carlo
simulations and our methods are applied to model amount of time sober for
patients leaving a rehabilitation center.

30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
1
The categories of this paper are: cs
15
The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ