Modeling Probabilities of Patent Oppositions in a Bayesian Semiparametric Regression Framework


In this paper, we apply a semiparametric approach described in Fahrmeir & Lang (2001b) and Brezger & Lang (2005) to analyze the determinants and the effects of patent oppositions in Europe. This approach replaces linear effects χ′β of metrical covariates χ by smooth regression functions f(χ). Within a Bayesian framework we apply MCMC-methods for estimation purposes. In order to analyze the benefits from applying semi-parametric models we compare our specification to the results of a simple linear probit model employed by Graham et al. (2002) using their dataset on EPO patents from the biotechnology/pharmaceutical and semiconductor/computer software sector.


Receiver Operating Characteristic Curve Deviance Information Criterion Patent Citation Patent System Metrical Covariates 
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