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Modeling Probabilities of Patent Oppositions in a Bayesian Semiparametric Regression Framework

Abstract

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.

Keywords

Receiver Operating Characteristic Curve Deviance Information Criterion Patent Citation Patent System Metrical Covariates 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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