Regularized regression when covariates are linked on a network: the 3CoSE algorithm

Matthias Weber, Jonas Striaukas, Martin Schumacher and Harald Binder

No 2021022, LIDAM Reprints LFIN from Université catholique de Louvain, Louvain Finance (LFIN)

Abstract: Covariates in regressions may be linked to each other on a network. Knowledge of the network structure can be incorporated into regularized regression settings via a network penalty term. However, when it is unknown whether the connection signs in the network are positive (connected covariates reinforce each other) or negative (connected covariates repress each other), the connection signs have to be estimated jointly with the covariate coefficients. This can be done with an algorithm iterating a connection sign estimation step and a covariate coefficient estimation step. We develop such an algorithm, called 3CoSE, and show detailed simulation results and an application forecasting event times. The algorithm performs well in a variety of settings. We also briefly describe the publicly available R-package developed for this purpose.

Keywords: Regressions on networks; network penalty; high-dimensional data; machine learning (search for similar items in EconPapers)
Pages: 20
Date: 2021-10-07
Note: In: Journal of Applied Statistics, 2021
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Citations: View citations in EconPapers (2)

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Journal Article: Regularized regression when covariates are linked on a network: the 3CoSE algorithm (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ajf:louvlr:2021022

DOI: 10.1080/02664763.2021.1982878

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