A smooth dynamic network model for patent collaboration data

09/02/2019

∙

by   Verena Bauer, et al.

∙

0

∙

share

The development and application of models, which take the evolution of networks with a dynamical structure into account are receiving increasing attention. Our research focuses on a profile likelihood approach to model time-stamped event data for a large-scale network applied on patent collaborations. As event we consider the submission of a joint patent and we investigate the driving forces for collaboration between inventors. We propose a flexible semiparametric model, which allows to include covariates built from the network (i.e. collaboration) history.