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

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

*Corresponding author af dette arbejde

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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 purpos
OriginalsprogEngelsk
TidsskriftJournal of Applied Statistics
Vol/bind50
Udgave nummer3
Sider (fra-til)535-554
Antal sider20
ISSN0266-4763
DOI
StatusUdgivet - 2023
Udgivet eksterntJa

Emneord

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

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