Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse least trimmed squares regression for analyzing high-dimensional
  large data sets   Sparse model estimation is a topic of high importance in modern data analysis
due to the increasing availability of data sets with a large number of
variables. Another common problem in applied statistics is the presence of
outliers in the data. This paper combines robust regression and sparse model
estimation. A robust and sparse estimator is introduced by adding an $L_1$
penalty on the coefficient estimates to the well-known least trimmed squares
(LTS) estimator. The breakdown point of this sparse LTS estimator is derived,
and a fast algorithm for its computation is proposed. In addition, the sparse
LTS is applied to protein and gene expression data of the NCI-60 cancer cell
panel. Both a simulation study and the real data application show that the
sparse LTS has better prediction performance than its competitors in the
presence of leverage points.

6
The categories of this paper are: astro-ph
1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat