caa a22 4500 467896372 CHVBK 20180406152805.0 cr unu---uuuuu 170328e20060601xx s 000 0 eng 10.1007/s00181-005-0047-0 doi (NATIONALLICENCE)springer-10.1007/s00181-005-0047-0 Modeling probabilities of patent oppositions in a Bayesian semiparametric regression framework [Elektronische Daten] [Alexander Jerak, Stefan Wagner] Previous econometric analyses of patent data rely on regression methods using purely parametric forms of the predictor for modeling the dependence of the response. These approaches lack the capability of identifying potential non-linear relationships between dependent and independent variables. In this paper, we present a Bayesian semiparametric approach making use of Markov Chain Monte Carlo (MCMC) simulation techniques which is able to capture these non-linearities. Using this methodology we reanalyze the determinants of patent oppositions in Europe for biotechnology/pharmaceutical and semiconductor/computer software patents. Our semiparametric specification clearly finds considerable non-linearities in the effect of various metrical covariates which has been not been discussed previously. Further, a formal model validation based on ROC-methodology which splits the data in a training and a validation data set shows a significant improvement of the explanatory and the predictive power of our approach compared to purely parametric specifications. Springer-Verlag, 2006 Markov Chain Monte Carlo nationallicence Bayesian semiparametric binary regression nationallicence Bayesian P-splines nationallicence Patent opposition nationallicence Jerak Alexander Department of Statistics, University of Munich, Ludwigstr. 33, 80539, Munich, Germany aut Wagner Stefan Department of Business Administration, INNO-tec, University of Munich, Kaulbachstr. 45, 80539, Munich, Germany aut Empirical Economics Physica-Verlag 31/2(2006-06-01), 513-533 0377-7332 31:2<513 2006 31 181 https://doi.org/10.1007/s00181-005-0047-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00181-005-0047-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Jerak Alexander Department of Statistics, University of Munich, Ludwigstr. 33, 80539, Munich, Germany aut NATIONALLICENCE 700 1- Wagner Stefan Department of Business Administration, INNO-tec, University of Munich, Kaulbachstr. 45, 80539, Munich, Germany aut NATIONALLICENCE 773 0- Empirical Economics Physica-Verlag 31/2(2006-06-01), 513-533 0377-7332 31:2<513 2006 31 181 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer