Locally Adaptive Function Estimation for Binary Regression Models - Alexander Jerak; Stefan Lang (Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, 2003)
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.
urn:nbn:de:bvb:19-epub-1691-7
Keywords: 519; adaptive smoothing; forest health data; highly oscillating functions; MCMC; random walk priors; unsmooth functions; variable smoothing parameter