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Network-Constrained Covariate Coefficient and Connection Sign Estimation

Matthias WeberJonas StriaukasMartin SchumacherHarald Binder

出版	School of Finance, University of St. Gallen, 2019
URL	http://books.google.com.hk/books?id=QHTIzQEACAAJ&hl=&source=gbs_api

註釋

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 via a network penalty term. 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 develop such an algorithm 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 R-package that we developed for this purpose, which is publicly available.