Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse Unit-Sum Regression   This paper considers sparsity in linear regression under the restriction that
the regression weights sum to one. We propose an approach that combines
$\ell_0$- and $\ell_1$-regularization. We compute its solution by adapting a
recent methodological innovation made by Bertsimas et al. (2016) for
$\ell_0$-regularization in standard linear regression. In a simulation
experiment we compare our approach to $\ell_0$-regularization and
$\ell_1$-regularization and find that it performs favorably in terms of
predictive performance and sparsity. In an application to index tracking we
show that our approach can obtain substantially sparser portfolios compared to
$\ell_1$-regularization while maintaining a similar tracking performance.

21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th