Locally Adaptive Function Estimation for Binary Regression Models

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.