Network constrained covariate coefficient and connection sign estimation
(2018) , 20 pages
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- 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 analgorithm. 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.
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Citations
Weber, M., Striaukas, J., Schumacher, M., & Binder, H. (2018). Network constrained covariate coefficient and connection sign estimation (CORE Discussion Paper 2018/18). https://hdl.handle.net/2078.5/268985