A smooth dynamic network model for patent collaboration data
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AbstractThe development and application of models, which take the evolution of network dynamics into account, are receiving increasing attention. We contribute to this field and focus on a profile likelihood approach to model time-stamped event data for a large-scale dynamic network. We investigate the collaboration of inventors using EU patent data. As event we consider the submission of a joint patent and we explore the driving forces for collaboration between inventors. We propose a flexible semiparametric model, which includes external and internal covariates, where the latter are built from the network history.
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2014 ◽
Vol 989-994
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pp. 2639-2642
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2016 ◽
Vol 463
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pp. 131-138
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2015 ◽
Vol 72
(5)
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pp. 1153-1176
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2018 ◽
Vol 14
(3)
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pp. 663-695
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2012 ◽
Vol 468
(2141)
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pp. 1332-1355
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