Network constrained covariate coefficient and connection sign estimation
Jonas Striaukas (),
Martin Schumacher and
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Martin Schumacher: Institute of Medical Biometry and Statistics, University of Freiburg
Harald Binder: Institute of Medical Biometry and Statistics, University of Freiburg
No 8, Bank of Lithuania Discussion Paper Series from Bank of Lithuania
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)
Pages: 23 pages
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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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