Bank of Lithuania
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2018-06-20

No 8. Matthias Weber, Jonas Striaukas, Martin Schumacher, Harald Binder. Network constrained covariate coefficient and connection sign estimation

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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.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

Matthias Weber, Jonas Striaukas