Alexander Jerak

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Special thanks go to (in alphabetical order of first names): Achim Zeileis for advertising HCL colors; Dieter Gollnow for computing and providing the map of Munich (a really hard job); Leo Held for advertising the program; Ludwig Fahrmeir for his patience with finishing the program and for carefully reading and correcting the manual; Ngianga-Bakwin Kandala(More)
Most econometric analyses of patent data rely on regression methods using a parametric form of the predictor for modeling the dependence of the response given certain covariates. These methods often lack the capability of identifying non-linear relationships between dependent and independent variables. We present an approach based on a generalized additive(More)
In this paper we present a nonparametric Bayesian approach for fitting unsmooth or highly oscillating functions in regression models with binary responses. The approach extends previous work by Lang et al. for Gaussian responses. Nonlinear functions are modelled by first or second order random walk priors with locally varying variances or smoothing(More)
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