Network constrained covariate coefficient and connection sign estimation

Matthias Weber, Jonas Striaukas, Martin, Schumacher and Binder, Harald
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Martin, Schumacher: University of Freiburg
Binder, Harald: University of Freiburg

No 2018018, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: Often, variables are linked to each other via a network. When such a network structure is known, this knowledge can be incorporated into regularized regression settings. In particular, an additional network penalty can be added on top of another penalty term, such as a Lasso penalty. However, when the type of interaction via the network is unknown (that is, whether connections are of an activating or a repressing type), the connection signs have to be estimated simultaneously with the covariate coefficients. This can be done with an algorithm iterating a connection sign estimation step and a covariate coefficient estimation step. We show detailed simulation results of such an algorithm. The algorithm performs well in a variety of settings. We also briefly describe the R-package that we developed for this purpose, which is publicly available.

Keywords: network regression; network penalty; connection sign estimation; regularized regression (search for similar items in EconPapers)
Date: 2018-06-11
New Economics Papers: this item is included in nep-ecm
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https://sites.uclouvain.be/core/publications/coredp/coredp2018.html (application/pdf)

Related works:
Working Paper: Network-Constrained Covariate Coefficient and Connection Sign Estimation (2020)
Working Paper: Network constrained covariate coefficient and connection sign estimation (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2018018

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