Regularized Regression When Covariates are Linkes on a Network: The 3CoSE Algortihm

Matthias Weber, Jonas Striaukas*, Martin Schumacher, Harald Binder

*Corresponding author for this work

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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 purpos
Original languageEnglish
JournalJournal of Applied Statistics
Issue number3
Pages (from-to)535-554
Number of pages20
Publication statusPublished - 2023
Externally publishedYes

Bibliographical note

Published online: 7 October 2022.


  • Regression on networks
  • Networks penalty
  • High-dimensional data
  • Machine learning

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