Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/23868
Authors: 
Jerak, Alexander
Lang, Stefan
Year of Publication: 
2003
Series/Report no.: 
Discussion papers / Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 310
Abstract: 
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. (2002) for Gaussian responses. Nonlinear functions are modelled by first or second order random walk priors with locally varying variances or smoothing parameters. Estimation is fully Bayesian and uses latent utility representations of binary regression models for efficient block sampling from the full conditionals of nonlinear functions.
Subjects: 
adaptive smoothing
forest health data
highly oscillating functions
MCMC
random walk priors
unsmooth functions
variable smoothing parameter
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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