title: Locally adaptive function estimation for binary regression models.
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. 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.
authors.affiliation: Department of Statistics, University of Munich, Ludwigstr. 33, 80539 Munich, Germany.
authors.forename: Alexander
authors.initials: A
authors.lastname: Jerak
authors.forename: Stefan
authors.initials: S
authors.lastname: Lang
mesh_terms: D001499:Bayes Theorem; D001699:Biometry; D016468:Forestry / Q000706:statistics & numerical data; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D014197:Trees